Uses of Interface
elki.database.relation.Relation

Packages that use Relation

Package	Description
elki.algorithm	Miscellaneous algorithms.
elki.algorithm.statistics	Statistical analysis algorithms.
elki.application.benchmark	Benchmarking pseudo algorithms.
elki.application.greedyensemble	Greedy ensembles for outlier detection.
elki.classification	Classification algorithms.
elki.clustering	Clustering algorithms.
elki.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.correlation.cash	Helper classes for the CASH algorithm.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.predicates	Neighbor and core predicated for Generalized DBSCAN.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.em.models
elki.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.kcenter	K-center clustering.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.kmedoids	K-medoids clustering (PAM).
elki.clustering.kmedoids.initialization
elki.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
elki.clustering.onedimensional	Clustering algorithms for one-dimensional data.
elki.clustering.optics	OPTICS family of clustering algorithms.
elki.clustering.silhouette	Silhouette clustering algorithms.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.clustering.svm
elki.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings.
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.data	Basic classes for different data types, database object types and label types.
elki.data.model	Cluster models classes for various algorithms.
elki.database	ELKI database layer - loading, storing, indexing and accessing data.
elki.database.query	Database queries - computing distances, neighbors, similarities - API and general documentation.
elki.database.query.distance	Prepared queries for distances.
elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
elki.database.query.range	Prepared queries for ε-range queries, that return all objects within the radius ε.
elki.database.query.rknn	Prepared queries for reverse k nearest neighbor (rkNN) queries.
elki.database.query.similarity	Prepared queries for similarity functions.
elki.database.relation	Relations, materialized and virtual (views).
elki.distance	Distance functions for use within ELKI.
elki.distance.adapter	Distance functions deriving distances from, e.g., similarity measures.
elki.distance.external	Distance functions using external data sources.
elki.distance.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
elki.distance.set	Distance functions for binary and set type data.
elki.distance.subspace	Distance functions based on subspaces.
elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
elki.index	Index structure implementations.
elki.index.distancematrix	Precomputed distance matrix.
elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
elki.index.invertedlist	Indexes using inverted lists.
elki.index.laesa	Linear Approximating and Eliminating Search Algorithm (LAESA).
elki.index.lsh	Locality Sensitive Hashing.
elki.index.lsh.hashfamilies	Hash function families for LSH.
elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
elki.index.preprocessed.snn	Indexes providing nearest neighbor sets.
elki.index.projected	Projected indexes for data.
elki.index.tree.betula	BETULA clustering by aggregating the data into cluster features.
elki.index.tree.metrical.covertree	Cover-tree variations.
elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries.
elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree.
elki.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree.
elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree.
elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree.
elki.index.tree.metrical.mtreevariants.mtree	MTree.
elki.index.tree.metrical.vptree
elki.index.tree.spatial.kd	K-d-tree and variants.
elki.index.tree.spatial.kd.split
elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree.
elki.index.tree.spatial.rstarvariants.flat	FlatRStarTree.
elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree.
elki.index.tree.spatial.rstarvariants.rstar	RStarTree.
elki.index.vafile	Vector Approximation File.
elki.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
elki.math	Mathematical operations and utilities used throughout the framework.
elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and eigenvector processing.
elki.math.scales	Scales handling for plotting.
elki.math.spacefillingcurves	Space filling curves.
elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
elki.outlier	Outlier detection algorithms.
elki.outlier.anglebased	Angle-based outlier detection algorithms.
elki.outlier.clustering	Clustering based outlier detection.
elki.outlier.density	Density-based outlier detection algorithms.
elki.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
elki.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
elki.outlier.lof	LOF family of outlier detection algorithms.
elki.outlier.lof.parallel	Parallelized variants of LOF.
elki.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
elki.outlier.spatial	Spatial outlier detection algorithms.
elki.outlier.spatial.neighborhood	Spatial outlier neighborhood classes.
elki.outlier.spatial.neighborhood.weighted	Weighted neighborhood definitions.
elki.outlier.subspace	Subspace outlier detection methods.
elki.outlier.svm	Support-Vector-Machines for outlier detection.
elki.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
elki.projection	Data projections (see also preprocessing filters for basic projections).
elki.result	Result types, representation and handling.
elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...).
elki.similarity	Similarity functions.
elki.similarity.cluster	Similarity measures for comparing clusters.
elki.similarity.kernel	Kernel functions.
elki.timeseries	Algorithms for change point detection in time series.
elki.utilities.referencepoints	Package containing strategies to obtain reference points.
elki.visualization	Visualization package of ELKI.
elki.visualization.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms.
elki.visualization.visualizers.parallel	Visualizers based on parallel coordinates.
elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots.
elki.visualization.visualizers.scatterplot.index	Visualizers for index structures based on 2D projections.
elki.visualization.visualizers.scatterplot.outlier	Visualizers for outlier scores based on 2D projections.
elki.visualization.visualizers.scatterplot.uncertain	Visualizers for uncertain data.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier	Tutorials on implementing outlier detection methods in ELKI.

Uses of Relation in elki.algorithm

Methods in elki.algorithm that return Relation

Modifier and Type	Method	Description
Relation<KNNList>	KNNJoin.autorun(Database database)
Relation<KNNList>	KNNJoin.run(Relation<? extends SpatialComparable> relation)	Joins in the given spatial database to each object its k-nearest neighbors.

Methods in elki.algorithm with parameters of type Relation

Modifier and Type	Method	Description
CorrelationAnalysisSolution	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids)	Runs the pca on the given set of IDs.
CorrelationAnalysisSolution	DependencyDerivator.generateModel(Relation<V> relation, DBIDs ids, double[] centroid)	Runs the pca on the given set of IDs and for the given centroid.
CorrelationAnalysisSolution	DependencyDerivator.run(Relation<V> relation)	Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.
KNNDistancesSampler.KNNDistanceOrderResult	KNNDistancesSampler.run(Relation<O> relation)	Provides an order of the kNN-distances for all objects within the specified database.
Relation<KNNList>	KNNJoin.run(Relation<? extends SpatialComparable> relation)	Joins in the given spatial database to each object its k-nearest neighbors.
WritableDataStore<KNNList>	KNNJoin.run(Relation<? extends SpatialComparable> relation, DBIDs ids)	Inner run method.

Uses of Relation in elki.algorithm.statistics

Methods in elki.algorithm.statistics with parameters of type Relation

Modifier and Type	Method	Description
private void	EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist, DBIDIter query, DistanceQuery<O> distQuery, Relation<O> relation)	Compute the distances to the neighbor objects.
protected double	HopkinsStatisticClusteringTendency.computeNNForRealData(KNNSearcher<DBIDRef> knnQuery, Relation<NumberVector> relation, int dim)	Search nearest neighbors for real data members.
void	EvaluateRetrievalPerformance.KNNEvaluator.evaluateKNN(double[] knnperf, ModifiableDoubleDBIDList nlist, Relation<?> lrelation, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<java.lang.Object> counters, java.lang.Object label)	Evaluate by simulating kNN classification for k=1...maxk
private DoubleMinMax	DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation, DistanceQuery<O> distance)	Compute the exact maximum and minimum.
private void	EvaluateRetrievalPerformance.findMatches(ModifiableDBIDs posn, Relation<?> lrelation, java.lang.Object label)	Find all matching objects.
protected void	HopkinsStatisticClusteringTendency.initializeDataExtends(Relation<NumberVector> relation, int dim, double[] min, double[] extend)	Initialize the uniform sampling area.
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector> rel)	Add scales to a single vector relation.
private ScalesResult	AddUniformScale.run(Relation<? extends NumberVector> rel)	Add scales to a single vector relation.
CollectionResult<double[]>	AveragePrecisionAtK.run(Relation<O> relation, Relation<?> lrelation)	Run the algorithm
CollectionResult<double[]>	DistanceQuantileSampler.run(Relation<O> relation)	Run the distance quantile sampler.
HistogramResult	DistanceStatisticsWithClasses.run(Database database, Relation<O> relation)
HistogramResult	EvaluateRankingQuality.run(Database database, Relation<V> relation)	Run the algorithm.
EvaluateRetrievalPerformance.RetrievalPerformanceResult	EvaluateRetrievalPerformance.run(Relation<O> relation, Relation<?> lrelation)	Run the algorithm
java.lang.Double	HopkinsStatisticClusteringTendency.run(Relation<NumberVector> relation)	Compute the Hopkins statistic for a vector relation.
HistogramResult	RankingQualityHistogram.run(Database database, Relation<O> relation)	Process a relation
private DoubleMinMax	DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation, DistanceQuery<O> distance)	Estimate minimum and maximum via sampling.

Uses of Relation in elki.application.benchmark

Methods in elki.application.benchmark with parameters of type Relation

Modifier and Type	Method	Description
private int	KNNBenchmark.run(KNNSearcher<DBIDRef> knnQuery, Relation<O> relation, Duration dur, MeanVariance mv, MeanVariance mvdist)	Run with the database as query source
private int	PrioritySearchBenchmark.run(PrioritySearcher<DBIDRef> priQuery, Relation<O> relation, Duration dur, MeanVariance mv, MeanVariance mvdist)	Run with the database as query source
protected int	RangeQueryBenchmark.run(RangeSearcher<DBIDRef> rangeQuery, Relation<O> relation, double radius, Duration dur, MeanVariance mv)	Run the algorithm, with constant radius
protected int	RangeQueryBenchmark.run(RangeSearcher<DBIDRef> rangeQuery, Relation<O> relation, Relation<NumberVector> radrel, Duration dur, MeanVariance mv)	Run the algorithm, with separate radius relation
protected int	RangeQueryBenchmark.run(RangeSearcher<O> rangeQuery, Relation<O> relation, DatabaseConnection queries, Duration dur, MeanVariance mv)	Run the algorithm, with a separate query set.

Uses of Relation in elki.application.greedyensemble

Methods in elki.application.greedyensemble that return Relation

Modifier and Type	Method	Description
static Relation<NumberVector>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip)	Prescale each vector (except when in skip) with the given scaling function.

Methods in elki.application.greedyensemble with parameters of type Relation

Modifier and Type	Method	Description
static Relation<NumberVector>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip)	Prescale each vector (except when in skip) with the given scaling function.

Uses of Relation in elki.classification

Fields in elki.classification declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends ClassLabel>	KNNClassifier.labelrep	Class label representation.

Methods in elki.classification with parameters of type Relation

Modifier and Type	Method	Description
void	Classifier.buildClassifier(Database database, Relation<? extends ClassLabel> classLabels)	Performs the training.
void	KNNClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labels)
void	PriorProbabilityClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labelrep)	Learns the prior probability for all classes.

Uses of Relation in elki.clustering

Methods in elki.clustering with parameters of type Relation

Modifier and Type	Method	Description
Clustering<MeanModel>	BetulaLeafPreClustering.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<PrototypeModel<O>>	CanopyPreClustering.run(Relation<O> relation)	Run the canopy clustering algorithm
Clustering<SimplePrototypeModel<DBID>>	CFSFDP.run(Relation<O> relation)	Perform CFSFDP clustering.
Clustering<PrototypeModel<O>>	Leader.run(Relation<O> relation)	Run the leader clustering algorithm.
Clustering<MeanModel>	NaiveMeanShiftClustering.run(Relation<V> relation)	Run the mean-shift clustering algorithm.
Clustering<Model>	SNNClustering.run(Relation<O> relation)	Perform SNN clustering

Uses of Relation in elki.clustering.affinitypropagation

Methods in elki.clustering.affinitypropagation with parameters of type Relation

Modifier and Type	Method	Description
double[][]	AffinityPropagationInitialization.getSimilarityMatrix(Relation<O> relation, ArrayDBIDs ids)	Compute the initial similarity matrix.
double[][]	DistanceBasedInitializationWithMedian.getSimilarityMatrix(Relation<O> relation, ArrayDBIDs ids)
double[][]	SimilarityBasedInitializationWithMedian.getSimilarityMatrix(Relation<O> relation, ArrayDBIDs ids)
Clustering<MedoidModel>	AffinityPropagation.run(Relation<O> relation)	Perform affinity propagation clustering.

Uses of Relation in elki.clustering.biclustering

Fields in elki.clustering.biclustering declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	AbstractBiclustering.relation	Relation we use.

Methods in elki.clustering.biclustering with parameters of type Relation

Modifier and Type	Method	Description
Clustering<M>	AbstractBiclustering.run(Relation<? extends NumberVector> relation)	Prepares the algorithm for running on a specific database.

Uses of Relation in elki.clustering.correlation

Fields in elki.clustering.correlation declared as Relation

Modifier and Type	Field	Description
private Relation<ParameterizationFunction>	CASH.fulldatabase	The entire relation.
private Relation<? extends NumberVector>	HiCO.Instance.relation	Data relation.

Methods in elki.clustering.correlation that return Relation

Modifier and Type	Method	Description
private Relation<DoubleVector>	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, DBIDs ids)	Builds a database for the derivator consisting of the ids in the specified interval.
private Relation<ParameterizationFunction>	CASH.preprocess(Relation<? extends NumberVector> vrel)	Preprocess the dataset, precomputing the parameterization functions.

Methods in elki.clustering.correlation with parameters of type Relation

Modifier and Type	Method	Description
private void	ORCLUS.assign(Relation<? extends NumberVector> database, java.util.List<ORCLUS.ORCLUSCluster> clusters)	Creates a partitioning of the database by assigning each object to its closest seed.
private MaterializedRelation<ParameterizationFunction>	CASH.buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation)	Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Relation<DoubleVector>	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, DBIDs ids)	Builds a database for the derivator consisting of the ids in the specified interval.
private double[]	CASH.determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality)	Determines the minimum and maximum function value of all parameterization functions stored in the specified database.
private static int	CASH.dimensionality(Relation<ParameterizationFunction> relation)	Get the dimensionality of a vector field.
private Clustering<Model>	CASH.doRun(Relation<ParameterizationFunction> relation, FiniteProgress progress)	Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<? extends NumberVector> relation, int dimensionality, ERiCNeighborPredicate.Instance npred)	Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
private double[][]	ORCLUS.findBasis(Relation<? extends NumberVector> database, ORCLUS.ORCLUSCluster cluster, int dim)	Finds the basis of the subspace of dimensionality dim for the specified cluster.
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<? extends NumberVector> relation, DBIDs currentids, int dimension, java.util.Random r)	This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
private void	CASH.initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids)	Initializes the heap with the root intervals.
private java.util.List<ORCLUS.ORCLUSCluster>	ORCLUS.initialSeeds(Relation<? extends NumberVector> database, int k)	Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Relation<? extends NumberVector> relation, java.util.List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress)	Reduces the number of seeds to k_new
private Relation<ParameterizationFunction>	CASH.preprocess(Relation<? extends NumberVector> vrel)	Preprocess the dataset, precomputing the parameterization functions.
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim)	Computes the projected energy of the specified clusters.
Clustering<Model>	CASH.run(Relation<? extends NumberVector> rel)	Run CASH on the relation.
Clustering<DimensionModel>	COPAC.run(Database database, Relation<? extends NumberVector> relation)	Run the COPAC algorithm.
Clustering<CorrelationModel>	ERiC.run(Database database, Relation<? extends NumberVector> relation)	Performs the ERiC algorithm on the given database.
ClusterOrder	HiCO.run(Relation<? extends NumberVector> relation)	Run the HiCO algorithm.
Clustering<Model>	LMCLUS.run(Relation<? extends NumberVector> relation)	The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	ORCLUS.run(Relation<? extends NumberVector> relation)	Performs the ORCLUS algorithm on the given database.
private double[][]	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs outids)	Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private LinearEquationSystem	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids)	Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private ORCLUS.ORCLUSCluster	ORCLUS.union(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim)	Returns the union of the two specified clusters.

Constructors in elki.clustering.correlation with parameters of type Relation

Constructor	Description
Instance(Relation<? extends NumberVector> relation)	Constructor.

Uses of Relation in elki.clustering.correlation.cash

Fields in elki.clustering.correlation.cash declared as Relation

Modifier and Type	Field	Description
private Relation<ParameterizationFunction>	CASHIntervalSplit.database	The database storing the parameterization functions.

Constructors in elki.clustering.correlation.cash with parameters of type Relation

Constructor	Description
CASHIntervalSplit(Relation<ParameterizationFunction> database, int minPts)	Initializes the logger and sets the debug status to the given value.

Uses of Relation in elki.clustering.dbscan

Methods in elki.clustering.dbscan with parameters of type Relation

Modifier and Type	Method	Description
protected void	GriDBSCAN.Instance.buildGrid(Relation<V> relation, int numcells, double[] offset)	Build the data grid.
protected void	DBSCAN.Instance.run(Relation<O> relation, RangeSearcher<DBIDRef> rangeSearcher)	Run the DBSCAN algorithm
Clustering<Model>	DBSCAN.run(Relation<O> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.Instance.run(Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	LSDBC.run(Relation<O> relation)	Run the LSDBC algorithm
private int	GriDBSCAN.Instance.runDBSCANOnCell(DBIDs cellids, Relation<V> relation, ModifiableDoubleDBIDList neighbors, ArrayModifiableDBIDs activeSet, int clusterid)

Uses of Relation in elki.clustering.dbscan.predicates

Fields in elki.clustering.dbscan.predicates declared as Relation

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	ERiCNeighborPredicate.Instance.relation	Vector data relation.

Methods in elki.clustering.dbscan.predicates with parameters of type Relation

Modifier and Type	Method	Description
protected abstract M	AbstractRangeQueryNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends O> relation)	Method to compute the actual data model.
protected COPACNeighborPredicate.COPACModel	COPACNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList knnneighbors, Relation<? extends NumberVector> relation)	COPAC model computation
protected PreDeConNeighborPredicate.PreDeConModel	FourCNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
protected PreDeConNeighborPredicate.PreDeConModel	PreDeConNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
COPACNeighborPredicate.Instance	COPACNeighborPredicate.instantiate(Relation<? extends NumberVector> relation)	Full instantiation method.
ERiCNeighborPredicate.Instance	ERiCNeighborPredicate.instantiate(Relation<? extends NumberVector> relation)	Full instantiation interface.
DataStore<M>	AbstractRangeQueryNeighborPredicate.preprocess(java.lang.Class<? super M> modelcls, Relation<? extends O> relation, RangeSearcher<DBIDRef> query)	Perform the preprocessing step.

Constructors in elki.clustering.dbscan.predicates with parameters of type Relation

Constructor	Description
Instance(DBIDs ids, DataStore<PCAFilteredResult> storage, Relation<? extends NumberVector> relation)	Constructor.

Uses of Relation in elki.clustering.em

Methods in elki.clustering.em with parameters of type Relation

Modifier and Type	Method	Description
private void	KDTreeEM.KDTree.aggregateStats(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int dim)	Aggregate the statistics for a leaf node.
private double[]	KDTreeEM.analyseDimWidth(Relation<? extends NumberVector> relation)	Helper method to retrieve the widths of all data in all dimensions.
double	BetulaGMM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, java.util.List<? extends BetulaClusterModel> models, WritableDataStore<double[]> probClusterIGivenX)	Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
static <O> double	EM.assignProbabilitiesToInstances(Relation<? extends O> relation, java.util.List<? extends EMClusterModel<? super O,?>> models, WritableDataStore<double[]> probClusterIGivenX, WritableDoubleDataStore loglikelihoods)	Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
private void	KDTreeEM.KDTree.computeBoundingBox(Relation<? extends NumberVector> relation, DBIDArrayIter iter)	Compute the bounding box.
static <O> void	EM.recomputeCovarianceMatrices(Relation<? extends O> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<? extends EMClusterModel<? super O,?>> models, double prior)	Recompute the covariance matrixes.
Clustering<EMModel>	BetulaGMM.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<M>	EM.run(Relation<O> relation)	Performs the EM clustering algorithm on the given database.
Clustering<EMModel>	KDTreeEM.run(Relation<? extends NumberVector> relation)	Calculates the EM Clustering with the given values by calling makeStats and calculation the new models from the given results

Constructors in elki.clustering.em with parameters of type Relation

Constructor	Description
KDTree(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, int left, int right, double[] dimWidth, double mbw)	Constructor for a KDTree with statistics needed for KDTreeEM calculation.

Uses of Relation in elki.clustering.em.models

Methods in elki.clustering.em.models with parameters of type Relation

Modifier and Type	Method	Description
java.util.List<DiagonalGaussianModel>	DiagonalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<? extends EMClusterModel<O,M>>	EMClusterModelFactory.buildInitialModels(Relation<? extends O> relation, int k)	Build the initial models
java.util.List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<SphericalGaussianModel>	SphericalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookMultivariateGaussianModel>	TextbookMultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookSphericalGaussianModel>	TextbookSphericalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TwoPassMultivariateGaussianModel>	TwoPassMultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)

Uses of Relation in elki.clustering.hierarchical

Methods in elki.clustering.hierarchical with parameters of type Relation

Modifier and Type	Method	Description
private void	LinearMemoryNNChain.Instance.nnChainCore(DBIDArrayIter aIt, DBIDArrayIter aIt2, ClusterMergeHistoryBuilder builder, Relation<O> rel)	Core function of NNChain.
ClusterMergeHistory	AGNES.run(Relation<O> relation)	Run the algorithm
ClusterMergeHistory	Anderberg.run(Relation<O> relation)
ClusterPrototypeMergeHistory	HACAM.run(Relation<O> relation)	Run the algorithm
ClusterDensityMergeHistory	HDBSCANLinearMemory.run(Relation<O> relation)	Run the algorithm
ClusterMergeHistory	LinearMemoryNNChain.Instance.run(ArrayDBIDs ids, Relation<O> relation, ClusterMergeHistoryBuilder builder)
ClusterMergeHistory	LinearMemoryNNChain.run(Relation<O> relation)	Run the NNchain algorithm.
ClusterPrototypeMergeHistory	MedoidLinkage.run(Relation<O> relation)	Run the algorithm
ClusterPrototypeMergeHistory	MiniMax.run(Relation<O> relation)	Run the algorithm on a database.
ClusterPrototypeMergeHistory	MiniMaxAnderberg.run(Relation<O> relation)	Run the algorithm
ClusterPrototypeMergeHistory	MiniMaxNNChain.run(Relation<O> relation)	Run the algorithm
ClusterMergeHistory	NNChain.run(Relation<O> relation)
ClusterMergeHistory	SLINK.run(Relation<O> relation)	Performs the SLINK algorithm on the given database.
ClusterMergeHistory	SLINKHDBSCANLinearMemory.run(Relation<O> relation)	Run the algorithm
private void	SLINK.step2primitive(DBIDRef id, DBIDArrayIter it, int n, Relation<? extends O> relation, PrimitiveDistance<? super O> distance, WritableDoubleDataStore m)	Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

Uses of Relation in elki.clustering.hierarchical.birch

Methods in elki.clustering.hierarchical.birch with parameters of type Relation

Modifier and Type	Method	Description
CFTree	CFTree.Factory.newTree(DBIDs ids, Relation<? extends NumberVector> relation)	Make a new tree.
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<KMeansModel>	BIRCHLloydKMeans.run(Relation<NumberVector> relation)	Run the clustering algorithm.

Uses of Relation in elki.clustering.kcenter

Methods in elki.clustering.kcenter with parameters of type Relation

Modifier and Type	Method	Description
Clustering<SimplePrototypeModel<O>>	GreedyKCenter.run(Relation<O> relation)	Perform greedy k-center clustering on the relation.

Uses of Relation in elki.clustering.kmeans

Fields in elki.clustering.kmeans declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	AbstractKMeans.Instance.relation	Data relation.

Methods in elki.clustering.kmeans with parameters of type Relation

Modifier and Type	Method	Description
double	FuzzyCMeans.assignProbabilitiesToInstances(Relation<V> relation, double[][] centers, WritableDataStore<double[]> probClusterIGivenX)	Calculates the weights of all points and clusters.
Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult(boolean varstat, Relation<? extends NumberVector> relation)	Build the result, recomputing the cluster variance if varstat is set to true.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeBoundedMidpoint(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using bounded midpoint splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeMedian(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using median splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeMidpoint(Relation<? extends NumberVector> relation, int left, int right)	Build the k-d-tree using midpoint splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeSSQ(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using a variance-based splitting strategy.
private static double[][]	AbstractKMeans.denseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected double[][]	AbstractKMeans.initialMeans(Relation<V> relation)	Choose the initial means.
protected static double[][]	AbstractKMeans.means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected double[][]	KMediansLloyd.Instance.medians(java.util.List<? extends DBIDs> clusters, double[][] medians, Relation<? extends NumberVector> relation)	Returns the median vectors of the given clusters in the given database.
protected void	AbstractKMeans.Instance.recomputeVariance(Relation<? extends NumberVector> relation)	Recompute the cluster variances.
Clustering<KMeansModel>	AnnulusKMeans.run(Relation<V> relation)
Clustering<M>	BestOfMultipleKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	BetulaLloydKMeans.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<M>	BisectingKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	CompareMeans.run(Relation<V> relation)
Clustering<KMeansModel>	ElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	ExponionKMeans.run(Relation<V> relation)
Clustering<MeanModel>	FuzzyCMeans.run(Relation<V> relation)	Runs Fuzzy C Means clustering on the given Relation
Clustering<KMeansModel>	HamerlyKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	HartiganWongKMeans.run(Relation<V> rel)
Clustering<KMeansModel>	KDTreeFilteringKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	KDTreePruningKMeans.run(Relation<V> relation)
Clustering<M>	KMeans.run(Relation<V> rel)	Run the clustering algorithm.
Clustering<KMeansModel>	KMeansMinusMinus.run(Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(Relation<V> relation)
Clustering<KMeansModel>	LloydKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	MacQueenKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	ShallotKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SimplifiedElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SingleAssignmentKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SortMeans.run(Relation<V> relation)
Clustering<M>	XMeans.run(Relation<V> relation)	Run the algorithm on a database and relation.
Clustering<KMeansModel>	YinYangKMeans.run(Relation<V> rel)
private static double[][]	AbstractKMeans.sparseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends SparseNumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected double[][]	GMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation)
protected double[][]	XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation)	Split an existing centroid into two initial centers.
protected java.util.List<Cluster<M>>	GMeans.splitCluster(Cluster<M> parentCluster, Relation<V> relation)
protected java.util.List<Cluster<M>>	XMeans.splitCluster(Cluster<M> parentCluster, Relation<V> relation)	Conditionally splits the clusters based on the information criterion.
private double	FuzzyCMeans.updateMeans(Relation<V> relation, WritableDataStore<double[]> probClusterIGivenX, double[][] means, int d)	Updates the means according to the weighted means of all data points.

Constructors in elki.clustering.kmeans with parameters of type Relation

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means, int t)	Constructor.
KDNode(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int start, int end)	Constructor.

Uses of Relation in elki.clustering.kmeans.initialization

Fields in elki.clustering.kmeans.initialization declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	KMC2.Instance.relation	Data relation.
protected Relation<? extends NumberVector>	KMeansPlusPlus.NumberVectorInstance.relation	Data relation.
protected Relation<? extends NumberVector>	SphericalKMeansPlusPlus.Instance.relation	Data relation.

Methods in elki.clustering.kmeans.initialization with parameters of type Relation

Modifier and Type	Method	Description
double[][]	AFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestSumPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FirstK.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMeansInitialization.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)	Choose initial means
double[][]	KMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Ostrovsky.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Predefined.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomlyChosen.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomNormalGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomUniformGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SampleKMeans.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalAFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalKMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Ostrovsky.NumberVectorInstance.run(Relation<? extends NumberVector> relation, int k)

Constructors in elki.clustering.kmeans.initialization with parameters of type Relation

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, int m, double alpha, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, double alpha, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.

Uses of Relation in elki.clustering.kmeans.parallel

Fields in elki.clustering.kmeans.parallel declared as Relation

Modifier and Type	Field	Description
private Relation<V>	KMeansProcessor.Instance.relation	Data relation.
(package private) Relation<V>	KMeansProcessor.relation	Data relation.

Methods in elki.clustering.kmeans.parallel with parameters of type Relation

Modifier and Type	Method	Description
Clustering<KMeansModel>	ParallelLloydKMeans.run(Relation<V> relation)

Constructors in elki.clustering.kmeans.parallel with parameters of type Relation

Constructor	Description
Instance(Relation<V> relation, NumberVectorDistance<? super V> distance, WritableIntegerDataStore assignment, double[][] means)	Constructor.
KMeansProcessor(Relation<V> relation, NumberVectorDistance<? super V> distance, WritableIntegerDataStore assignment, double[] varsum)	Constructor.

Uses of Relation in elki.clustering.kmeans.quality

Methods in elki.clustering.kmeans.quality with parameters of type Relation

Modifier and Type	Method	Description
static double	AbstractKMeansQualityMeasure.logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionXMeans.logLikelihoodXMeans(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering)	Compute the number of free parameters.
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)	Calculates and returns the quality measure.
<V extends NumberVector> double	WithinClusterMeanDistance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVariance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
static double	AbstractKMeansQualityMeasure.varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistance<?> distance, Relation<? extends NumberVector> relation)	Variance contribution of a single cluster.

Uses of Relation in elki.clustering.kmeans.spherical

Methods in elki.clustering.kmeans.spherical with parameters of type Relation

Modifier and Type	Method	Description
protected static double[][]	SphericalKMeans.Instance.means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected void	SphericalKMeans.Instance.recomputeVariance(Relation<? extends NumberVector> relation)
Clustering<KMeansModel>	EuclideanSphericalElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	EuclideanSphericalHamerlyKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	EuclideanSphericalSimplifiedElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalHamerlyKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalSimplifiedElkanKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalSimplifiedHamerlyKMeans.run(Relation<V> relation)
Clustering<KMeansModel>	SphericalSingleAssignmentKMeans.run(Relation<V> relation)

Constructors in elki.clustering.kmeans.spherical with parameters of type Relation

Constructor	Description
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.

Uses of Relation in elki.clustering.kmedoids

Methods in elki.clustering.kmedoids that return Relation

Modifier and Type	Method	Description
Relation<? extends V>	CLARA.CachedDistanceQuery.getRelation()

Methods in elki.clustering.kmedoids with parameters of type Relation

Modifier and Type	Method	Description
Clustering<MedoidModel>	AlternatingKMedoids.run(Relation<O> relation)
Clustering<MedoidModel>	AlternatingKMedoids.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	CLARA.run(Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(Relation<V> relation, int k, DistanceQuery<? super V> distQ)
Clustering<MedoidModel>	CLARANS.run(Relation<O> relation)	Run CLARANS clustering.
Clustering<MedoidModel>	CLARANS.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	EagerPAM.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastCLARA.run(Relation<V> relation)
Clustering<MedoidModel>	FastCLARA.run(Relation<V> relation, int k, DistanceQuery<? super V> distQ)
Clustering<MedoidModel>	FastCLARANS.run(Relation<V> relation)
Clustering<MedoidModel>	FasterCLARA.run(Relation<O> relation)
Clustering<MedoidModel>	FasterCLARA.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FasterPAM.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastPAM.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastPAM1.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	KMedoidsClustering.run(Relation<O> relation)	Run k-medoids clustering.
Clustering<MedoidModel>	KMedoidsClustering.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)	Run k-medoids clustering with a given distance query. Not a very elegant API, but needed for some types of nested k-medoids.
Clustering<MedoidModel>	PAM.run(Relation<O> relation)
Clustering<MedoidModel>	PAM.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	ReynoldsPAM.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	SingleAssignmentKMedoids.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)

Uses of Relation in elki.clustering.kmedoids.initialization

Methods in elki.clustering.kmedoids.initialization with parameters of type Relation

Modifier and Type	Method	Description
double[][]	BUILD.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	LAB.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	ParkJun.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)

Uses of Relation in elki.clustering.meta

Methods in elki.clustering.meta with parameters of type Relation

Modifier and Type	Method	Description
private void	ExternalClustering.attachToRelation(Relation<?> r, it.unimi.dsi.fastutil.ints.IntArrayList assignment, java.util.ArrayList<java.lang.String> name)	Build a clustering from the file result.

Uses of Relation in elki.clustering.onedimensional

Methods in elki.clustering.onedimensional with parameters of type Relation

Modifier and Type	Method	Description
Clustering<ClusterModel>	KNNKernelDensityMinimaClustering.run(Relation<? extends NumberVector> relation)	Run the clustering algorithm on a data relation.

Uses of Relation in elki.clustering.optics

Methods in elki.clustering.optics with parameters of type Relation

Modifier and Type	Method	Description
abstract ClusterOrder	AbstractOPTICS.run(Relation<O> relation)	Run OPTICS on the database.
ClusterOrder	DeLiClu.run(Relation<V> relation)	Run the DeLiClu clustering algorithm.
ClusterOrder	FastOPTICS.run(Relation<V> relation)	Run the algorithm.
ClusterOrder	OPTICSHeap.run(Relation<O> relation)
ClusterOrder	OPTICSList.run(Relation<O> relation)

Constructors in elki.clustering.optics with parameters of type Relation

Constructor	Description
Instance(Relation<O> relation)	Constructor for a single data set.
Instance(Relation<O> relation)	Constructor for a single data set.

Uses of Relation in elki.clustering.silhouette

Methods in elki.clustering.silhouette with parameters of type Relation

Modifier and Type	Method	Description
Clustering<MedoidModel>	FasterMSC.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastMSC.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	PAMMEDSIL.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	PAMSIL.run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)

Uses of Relation in elki.clustering.subspace

Fields in elki.clustering.subspace declared as Relation

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	DiSH.Instance.relation	Data relation.
private Relation<? extends NumberVector>	HiSC.Instance.relation	Data relation.

Methods in elki.clustering.subspace with parameters of type Relation

Modifier and Type	Method	Description
private java.util.ArrayList<PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(ArrayDBIDs m_current, long[][] dimensions, Relation<? extends NumberVector> database)	Assigns the objects to the clusters.
private void	P3C.assignUnassigned(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned)	Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private double	PROCLUS.avgDistance(double[] centroid, DBIDs objectIDs, Relation<? extends NumberVector> database, int dimension)	Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Relation<? extends NumberVector> database, Clustering<SubspaceModel> clustering, java.util.List<Cluster<SubspaceModel>> clusters, int dimensionality)	Builds the cluster hierarchy.
private void	DiSH.checkClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel>	DiSH.computeClusters(Relation<? extends NumberVector> database, DiSH.DiSHClusterOrder clusterOrder)	Computes the hierarchical clusters according to the cluster order.
private void	P3C.computeFuzzyMembership(Relation<? extends NumberVector> relation, java.util.ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, int dim)	Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
protected boolean	DOC.dimensionIsRelevant(int dimension, Relation<? extends NumberVector> relation, DBIDs points)	Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).
private double	PROCLUS.evaluateClusters(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, long[][] dimensions, Relation<? extends NumberVector> database)	Evaluates the quality of the clusters.
private it.unimi.dsi.fastutil.objects.Object2ObjectOpenCustomHashMap<long[],java.util.List<ArrayModifiableDBIDs>>	DiSH.extractClusters(Relation<? extends NumberVector> relation, DiSH.DiSHClusterOrder clusterOrder)	Extracts the clusters from the cluster order.
private java.util.List<PROCLUS.PROCLUSCluster>	PROCLUS.finalAssignment(java.util.List<Pair<double[],long[]>> dimensions, Relation<? extends NumberVector> database)	Refinement step to assign the objects to the final clusters.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaceCandidates(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces)	Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaces(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces)	Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private long[][]	PROCLUS.findDimensions(ArrayDBIDs medoids, Relation<? extends NumberVector> relation, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)	Determines the set of correlated dimensions for each medoid in the specified medoid set.
private java.util.List<Pair<double[],long[]>>	PROCLUS.findDimensions(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, Relation<? extends NumberVector> database)	Refinement step that determines the set of correlated dimensions for each cluster centroid.
protected DBIDs	DOC.findNeighbors(DBIDRef q, long[] nD, ArrayModifiableDBIDs S, Relation<? extends NumberVector> relation)	Find the neighbors of point q in the given subspace
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector> database)	Determines the one-dimensional dense subspace candidates by making a pass over the database.
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaces(Relation<? extends NumberVector> database)	Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private void	P3C.findOutliers(Relation<? extends NumberVector> relation, java.util.List<MultivariateGaussianModel> models, java.util.ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise)	Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.
private Pair<long[],ArrayModifiableDBIDs>	DiSH.findParent(Relation<? extends NumberVector> relation, Pair<long[],ArrayModifiableDBIDs> child, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Returns the parent of the specified cluster
private java.util.Collection<CLIQUEUnit>	CLIQUE.initOneDimensionalUnits(Relation<? extends NumberVector> database)	Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Relation<? extends NumberVector> relation, Cluster<SubspaceModel> parent, It<Cluster<SubspaceModel>> iter, int db_dim)	Returns true, if the specified parent cluster is a parent of one child of the children clusters.
protected Cluster<SubspaceModel>	DOC.makeCluster(Relation<? extends NumberVector> relation, DBIDs C, long[] D)	Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private SetDBIDs[][]	P3C.partitionData(Relation<? extends NumberVector> relation, int bins)	Partition the data set into bins bins in each dimension independently.
Clustering<SubspaceModel>	CLIQUE.run(Relation<? extends NumberVector> relation)	Performs the CLIQUE algorithm on the given database.
Clustering<SubspaceModel>	DiSH.run(Relation<? extends NumberVector> relation)	Performs the DiSH algorithm on the given database.
Clustering<SubspaceModel>	DOC.run(Relation<? extends NumberVector> relation)	Performs the DOC or FastDOC (as configured) algorithm.
ClusterOrder	HiSC.run(Relation<? extends NumberVector> relation)	Run the HiSC algorithm
Clustering<SubspaceModel>	P3C.run(Relation<? extends NumberVector> relation)	Performs the P3C algorithm on the given Database.
<V extends NumberVector> Clustering<SubspaceModel>	PROCLUS.run(Relation<V> relation)	Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel>	SUBCLU.run(Relation<V> relation)	Performs the SUBCLU algorithm on the given database.
private java.util.List<Cluster<Model>>	SUBCLU.runDBSCAN(Relation<V> relation, DBIDs ids, Subspace subspace)	Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
protected Cluster<SubspaceModel>	DOC.runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)	Performs a single run of DOC, finding a single cluster.
protected Cluster<SubspaceModel>	FastDOC.runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)	Performs a single run of FastDOC, finding a single cluster.
private java.util.List<Cluster<SubspaceModel>>	DiSH.sortClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Constructors in elki.clustering.subspace with parameters of type Relation

Constructor	Description
Instance(Relation<? extends NumberVector> relation)	Constructor.
Instance(Relation<? extends NumberVector> relation)	Constructor.

Uses of Relation in elki.clustering.svm

Methods in elki.clustering.svm with parameters of type Relation

Modifier and Type	Method	Description
private boolean	SupportVectorClustering.checkConnectivity(Relation<NumberVector> relation, double[] start, DBIDRef destRef, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_squared)	Checks if the connecting line between start and dest lies inside the kernel space sphere.
Clustering<Model>	SupportVectorClustering.run(Relation<NumberVector> relation)	perform clustering

Uses of Relation in elki.clustering.trivial

Methods in elki.clustering.trivial with parameters of type Relation

Modifier and Type	Method	Description
private java.util.HashMap<java.lang.String,DBIDs>	ByLabelClustering.multipleAssignment(Relation<?> data)	Assigns the objects of the database to multiple clusters according to their labels.
Clustering<Model>	ByLabelClustering.run(Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	ByLabelHierarchicalClustering.run(Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	ByModelClustering.run(Relation<Model> relation)	Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(Relation<?> relation)	Perform trivial clustering.
Clustering<Model>	TrivialAllNoise.run(Relation<?> relation)	Run the trivial clustering algorithm.
private java.util.HashMap<java.lang.String,DBIDs>	ByLabelClustering.singleAssignment(Relation<?> data)	Assigns the objects of the database to single clusters according to their labels.

Uses of Relation in elki.clustering.uncertain

Fields in elki.clustering.uncertain declared as Relation

Modifier and Type	Field	Description
private Relation<? extends UncertainObject>	FDBSCANNeighborPredicate.Instance.relation	The relation holding the uncertain objects.

Methods in elki.clustering.uncertain with parameters of type Relation

Modifier and Type	Method	Description
protected boolean	UKMeans.assignToNearestCluster(Relation<DiscreteUncertainObject> relation, java.util.List<double[]> means, java.util.List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum)	Returns a list of clusters.
protected java.util.List<double[]>	UKMeans.means(java.util.List<? extends ModifiableDBIDs> clusters, java.util.List<double[]> means, Relation<DiscreteUncertainObject> database)	Returns the mean vectors of the given clusters in the given database.
C	CenterOfMassMetaClustering.run(Relation<? extends UncertainObject> relation)	This run method will do the wrapping.
Clustering<?>	RepresentativeUncertainClustering.run(Database database, Relation<? extends UncertainObject> relation)	This run method will do the wrapping.
Clustering<KMeansModel>	UKMeans.run(Relation<DiscreteUncertainObject> relation)	Run the clustering.

Constructors in elki.clustering.uncertain with parameters of type Relation

Constructor	Description
Instance(double epsilon, int sampleSize, double threshold, Relation<? extends UncertainObject> relation, RandomFactory rand)	Constructor.

Uses of Relation in elki.data

Fields in elki.data declared as Relation

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	VectorUtil.SortDBIDsBySingleDimension.data	The relation to sort.

Constructors in elki.data with parameters of type Relation

Constructor	Description
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data)	Constructor.
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data, int dim)	Constructor.

Uses of Relation in elki.data.model

Methods in elki.data.model with parameters of type Relation

Modifier and Type	Method	Description
static NumberVector	ModelUtil.getPrototype(Model model, Relation<? extends NumberVector> relation)	Get the representative vector for a cluster model.
static <V extends NumberVector> V	ModelUtil.getPrototype(Model model, Relation<? extends V> relation, NumberVector.Factory<V> factory)	Get (and convert!)
static NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids)	Get the representative vector for a cluster model, or compute the centroid.
static <V extends NumberVector> V	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends V> relation, DBIDs ids, NumberVector.Factory<V> factory)	Get the representative vector for a cluster model, or compute the centroid.

Constructors in elki.data.model with parameters of type Relation

Constructor	Description
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid)	Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid, java.text.NumberFormat nf)	Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.

Uses of Relation in elki.database

Fields in elki.database with type parameters of type Relation

Modifier and Type	Field	Description
protected java.util.List<Relation<?>>	AbstractDatabase.relations	The relations we manage.

Methods in elki.database that return Relation

Modifier and Type	Method	Description
private Relation<?>	HashmapDatabase.addNewRelation(SimpleTypeInformation<?> meta)	Add a new representation for the given meta.
protected Relation<?>[]	HashmapDatabase.alignColumns(ObjectBundle pack)	Find a mapping from package columns to database columns, eventually adding new database columns when needed.
<O> Relation<O>	AbstractDatabase.getRelation(TypeInformation restriction, java.lang.Object... hints)
<O> Relation<O>	Database.getRelation(TypeInformation restriction, java.lang.Object... hints)	Get an object representation.
static Relation<java.lang.String>	DatabaseUtil.guessLabelRepresentation(Database database)	Guess a potentially label-like representation, preferring class labels.
static Relation<java.lang.String>	DatabaseUtil.guessObjectLabelRepresentation(Database database)	Guess a potentially object label-like representation.

Methods in elki.database that return types with arguments of type Relation

Modifier and Type	Method	Description
java.util.Collection<Relation<?>>	AbstractDatabase.getRelations()
java.util.Collection<Relation<?>>	Database.getRelations()	Get all relations of a database.

Methods in elki.database with parameters of type Relation

Modifier and Type	Method	Description
void	ProxyDatabase.addRelation(Relation<?> relation)	Add a new representation.
static java.util.SortedSet<ClassLabel>	DatabaseUtil.getClassLabels(Relation<? extends ClassLabel> database)	Retrieves all class labels within the database.

Constructors in elki.database with parameters of type Relation

Constructor	Description
ProxyDatabase(DBIDs ids, Relation<?>... relations)	Constructor.

Constructor parameters in elki.database with type arguments of type Relation

Constructor	Description
ProxyDatabase(DBIDs ids, java.lang.Iterable<Relation<?>> relations)	Constructor.

Uses of Relation in elki.database.query

Fields in elki.database.query declared as Relation

Modifier and Type	Field	Description
private Relation<O>	QueryBuilder.relation	Relation to query.
private Relation<? extends O>	WrappedPrioritySearchDBIDByLookup.relation	Data relation.

Methods in elki.database.query with parameters of type Relation

Modifier and Type	Method	Description
<O> DistanceQuery<O>	EmpiricalQueryOptimizer.getDistanceQuery(Relation<? extends O> relation, Distance<? super O> distance, int flags)
default <O> DistanceQuery<O>	QueryOptimizer.getDistanceQuery(Relation<? extends O> relation, Distance<? super O> distanceFunction, int flags)	Optimize a distance query for this relation.
default <O> SimilarityQuery<O>	QueryOptimizer.getSimilarityQuery(Relation<? extends O> relation, Similarity<? super O> similarityFunction, int flags)	Optimize a similarity query for this relation.
<O> KNNSearcher<DBIDRef>	EmpiricalQueryOptimizer.kNNByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)
default <O> KNNSearcher<DBIDRef>	QueryOptimizer.kNNByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)	Optimize a kNN query for this relation.
<O> KNNSearcher<O>	EmpiricalQueryOptimizer.kNNByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)
default <O> KNNSearcher<O>	QueryOptimizer.kNNByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)	Optimize a kNN query for this relation.
private <O> DistancePriorityIndex<O>	EmpiricalQueryOptimizer.makeCoverTree(Relation<? extends O> relation, Distance<? super O> distance, int leafsize)
private <O> DistancePriorityIndex<O>	EmpiricalQueryOptimizer.makeKDTree(Relation<? extends O> relation, Distance<? super O> distance, int k)
private <O> KNNIndex<O>	EmpiricalQueryOptimizer.makeKnnPreprocessor(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)	Make a knn preprocessor.
private <O> DistancePriorityIndex<O>	EmpiricalQueryOptimizer.makeMatrixIndex(Relation<? extends O> relation, Distance<? super O> distance)
private <O> DistancePriorityIndex<O>	EmpiricalQueryOptimizer.makeVPTree(Relation<? extends O> relation, Distance<? super O> distance, int leafsize)
<O> PrioritySearcher<DBIDRef>	EmpiricalQueryOptimizer.priorityByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)
default <O> PrioritySearcher<DBIDRef>	QueryOptimizer.priorityByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)	Optimize a distance priority search for this relation.
<O> PrioritySearcher<O>	EmpiricalQueryOptimizer.priorityByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)
default <O> PrioritySearcher<O>	QueryOptimizer.priorityByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)	Optimize a distance priority search for this relation.
<O> RangeSearcher<DBIDRef>	EmpiricalQueryOptimizer.rangeByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)
default <O> RangeSearcher<DBIDRef>	QueryOptimizer.rangeByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)	Optimize a range query for this relation.
<O> RangeSearcher<O>	EmpiricalQueryOptimizer.rangeByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)
default <O> RangeSearcher<O>	QueryOptimizer.rangeByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, double maxrange, int flags)	Optimize a range query for this relation.
default <O> RKNNSearcher<DBIDRef>	QueryOptimizer.rkNNByDBID(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)	Optimize a reverse nearest neighbors query for this relation.
default <O> RKNNSearcher<O>	QueryOptimizer.rkNNByObject(Relation<? extends O> relation, DistanceQuery<O> distanceQuery, int maxk, int flags)	Optimize a reverse nearest neighbors query for this relation.
default <O> RangeSearcher<DBIDRef>	QueryOptimizer.similarityRangeByDBID(Relation<? extends O> relation, SimilarityQuery<O> simQuery, double maxrange, int flags)	Optimize a range query for this relation.
default <O> RangeSearcher<O>	QueryOptimizer.similarityRangeByObject(Relation<? extends O> relation, SimilarityQuery<O> simQuery, double maxrange, int flags)	Optimize a range query for this relation.
static <O> PrioritySearcher<DBIDRef>	WrappedPrioritySearchDBIDByLookup.wrap(Relation<? extends O> relation, PrioritySearcher<O> inner)	Wrap a query, if not null.

Constructors in elki.database.query with parameters of type Relation

Constructor	Description
Linear(Relation<? extends O> relation, PrioritySearcher<O> inner)	Constructor.
QueryBuilder(Relation<O> relation, Distance<? super O> distance)	Constructor.
QueryBuilder(Relation<O> relation, Similarity<? super O> similarity)	Constructor.
WrappedPrioritySearchDBIDByLookup(Relation<? extends O> relation, PrioritySearcher<O> inner)	Constructor.

Uses of Relation in elki.database.query.distance

Fields in elki.database.query.distance declared as Relation

Modifier and Type	Field	Description
protected Relation<DBID>	DBIDDistanceQuery.relation	Relation to query.
protected Relation<? extends O>	PrimitiveDistanceQuery.relation	The data to use for this query

Methods in elki.database.query.distance that return Relation

Modifier and Type	Method	Description
Relation<? extends DBID>	DBIDDistanceQuery.getRelation()
Relation<? extends O>	DistanceQuery.getRelation()	Access the underlying data query.
Relation<? extends O>	PrimitiveDistanceQuery.getRelation()

Constructors in elki.database.query.distance with parameters of type Relation

Constructor	Description
DBIDDistanceQuery(Relation<DBID> relation, DBIDDistance distanceFunction)	Constructor.
DBIDRangeDistanceQuery(Relation<DBID> relation, DBIDRangeDistance distanceFunction)	Constructor.
PrimitiveDistanceQuery(Relation<? extends O> relation, PrimitiveDistance<? super O> distanceFunction)	Constructor.
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, PrimitiveDistance<? super O> distanceFunction, PrimitiveSimilarity<? super O> similarityFunction)	Constructor.
SpatialPrimitiveDistanceQuery(Relation<? extends V> relation, SpatialPrimitiveDistance<? super V> distanceFunction)
SpatialPrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, SpatialPrimitiveDistance<? super O> distanceFunction, PrimitiveSimilarity<? super O> similarityFunction)	Constructor.

Uses of Relation in elki.database.query.knn

Fields in elki.database.query.knn declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends O>	LinearScanPrimitiveKNNByObject.relation	Relation to query.
protected Relation<?>	PreprocessorKNNQuery.relation	The data to use for this query
private Relation<? extends O>	WrappedKNNDBIDByLookup.relation	Data relation.

Methods in elki.database.query.knn with parameters of type Relation

Modifier and Type	Method	Description
static <O> KNNSearcher<DBIDRef>	WrappedKNNDBIDByLookup.wrap(Relation<? extends O> relation, KNNSearcher<O> inner)	Wrap a query, if not null.

Constructors in elki.database.query.knn with parameters of type Relation

Constructor	Description
Linear(Relation<? extends O> relation, KNNSearcher<O> inner)	Constructor.
PreprocessorKNNQuery(Relation<?> relation, AbstractMaterializeKNNPreprocessor<?> preprocessor)	Constructor.
PreprocessorSqrtKNNQuery(Relation<?> relation, AbstractMaterializeKNNPreprocessor<?> preprocessor)	Constructor.
PreprocessorSquaredKNNQuery(Relation<?> relation, AbstractMaterializeKNNPreprocessor<?> preprocessor)	Constructor.
WrappedKNNDBIDByLookup(Relation<? extends O> relation, KNNSearcher<O> inner)	Constructor.

Uses of Relation in elki.database.query.range

Fields in elki.database.query.range declared as Relation

Modifier and Type	Field	Description
private Relation<? extends O>	LinearScanEuclideanRangeByObject.relation	Relation to scan.
private Relation<? extends O>	WrappedRangeDBIDByLookup.relation	Data relation.

Methods in elki.database.query.range with parameters of type Relation

Modifier and Type	Method	Description
static <O> RangeSearcher<DBIDRef>	WrappedRangeDBIDByLookup.wrap(Relation<? extends O> relation, RangeSearcher<O> inner)	Wrap a query, if not null.

Constructors in elki.database.query.range with parameters of type Relation

Constructor	Description
Linear(Relation<? extends O> relation, RangeSearcher<O> inner)	Constructor.
WrappedRangeDBIDByLookup(Relation<? extends O> relation, RangeSearcher<O> inner)	Constructor.

Uses of Relation in elki.database.query.rknn

Fields in elki.database.query.rknn declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends O>	PreprocessorRKNNQuery.relation	The data to use for this query
private Relation<? extends O>	WrappedRKNNDBIDByLookup.relation	Data relation.

Methods in elki.database.query.rknn with parameters of type Relation

Modifier and Type	Method	Description
static <O> RKNNSearcher<DBIDRef>	WrappedRKNNDBIDByLookup.wrap(Relation<? extends O> relation, RKNNSearcher<O> inner)	Wrap a query, if not null.

Constructors in elki.database.query.rknn with parameters of type Relation

Constructor	Description
Linear(Relation<? extends O> relation, RKNNSearcher<O> inner)	Constructor.
PreprocessorRKNNQuery(Relation<O> database, MaterializeKNNAndRKNNPreprocessor.Factory<O> preprocessor)	Constructor.
PreprocessorRKNNQuery(Relation<O> relation, MaterializeKNNAndRKNNPreprocessor<O> preprocessor)	Constructor.
WrappedRKNNDBIDByLookup(Relation<? extends O> relation, RKNNSearcher<O> inner)	Constructor.

Uses of Relation in elki.database.query.similarity

Fields in elki.database.query.similarity declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends O>	PrimitiveSimilarityQuery.relation	The data to use for this query

Methods in elki.database.query.similarity that return Relation

Modifier and Type	Method	Description
Relation<? extends O>	PrimitiveSimilarityQuery.getRelation()
Relation<? extends O>	SimilarityQuery.getRelation()	Access the underlying data query.

Constructors in elki.database.query.similarity with parameters of type Relation

Constructor	Description
PrimitiveSimilarityQuery(Relation<? extends O> relation, PrimitiveSimilarity<? super O> similarityFunction)	Constructor.

Uses of Relation in elki.database.relation

Subinterfaces of Relation in elki.database.relation

Modifier and Type	Interface	Description
interface	DoubleRelation	Interface for double-valued relations.
interface	ModifiableRelation<O>	Relations that allow modification.

Classes in elki.database.relation that implement Relation

Modifier and Type	Class	Description
class	ConvertToStringView	Representation adapter that uses toString() to produce a string representation.
class	DBIDView	Pseudo-representation that is the object ID itself.
class	MaterializedDoubleRelation	Represents a single representation.
class	MaterializedRelation<O>	Represents a single representation.
class	ProjectedView<IN,OUT>	Projected relation view (non-materialized)
class	ProxyView<O>	A virtual partitioning of the database.

Fields in elki.database.relation declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends O>	RelationUtil.RelationObjectIterator.database	The database we use.
(package private) Relation<? extends O>	RelationUtil.CollectionFromRelation.db	The database we query.
(package private) Relation<?>	ConvertToStringView.existing	The database we use
private Relation<? extends IN>	ProjectedView.inner	The wrapped representation where we get the IDs from.
private Relation<O>	ProxyView.inner	The wrapped representation where we get the IDs from.

Methods in elki.database.relation that return Relation

Modifier and Type	Method	Description
static <V extends NumberVector,T extends NumberVector> Relation<V>	RelationUtil.relationUglyVectorCast(Relation<T> database)	An ugly vector type cast unavoidable in some situations due to Generics.

Methods in elki.database.relation with parameters of type Relation

Modifier and Type	Method	Description
static <V extends FeatureVector<?>> VectorFieldTypeInformation<V>	RelationUtil.assumeVectorField(Relation<V> relation)	Get the vector field type information from a relation.
static double[][]	RelationUtil.computeMinMax(Relation<? extends NumberVector> relation)	Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static int	RelationUtil.dimensionality(Relation<? extends SpatialComparable> relation)	Get the dimensionality of a database relation.
static <V extends SpatialComparable> java.lang.String	RelationUtil.getColumnLabel(Relation<? extends V> rel, int col)	Get the column name or produce a generic label "Column XY".
static <V extends NumberVector> NumberVector.Factory<V>	RelationUtil.getNumberVectorFactory(Relation<V> relation)	Get the number vector factory of a database relation.
static int	RelationUtil.maxDimensionality(Relation<? extends SpatialComparable> relation)	Get the dimensionality of a database relation.
static double[][]	RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation, ArrayDBIDs ids)	Copy a relation into a double matrix.
static <V extends NumberVector,T extends NumberVector> Relation<V>	RelationUtil.relationUglyVectorCast(Relation<T> database)	An ugly vector type cast unavoidable in some situations due to Generics.

Constructors in elki.database.relation with parameters of type Relation

Constructor	Description
CollectionFromRelation(Relation<? extends O> db)	Constructor.
ConvertToStringView(Relation<?> existing)	Constructor.
ProjectedView(Relation<? extends IN> inner, Projection<IN,OUT> projection)	Constructor.
ProxyView(DBIDs idview, Relation<O> inner)	Constructor.
RelationObjectIterator(DBIDIter iter, Relation<? extends O> database)	Full Constructor.
RelationObjectIterator(Relation<? extends O> database)	Simplified constructor.

Uses of Relation in elki.distance

Fields in elki.distance declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	AbstractDatabaseDistance.Instance.relation	Relation to query.
protected Relation<O>	AbstractIndexBasedDistance.Instance.relation	Relation to query.

Methods in elki.distance that return Relation

Modifier and Type	Method	Description
Relation<? extends O>	AbstractDatabaseDistance.Instance.getRelation()
Relation<? extends O>	AbstractIndexBasedDistance.Instance.getRelation()

Methods in elki.distance with parameters of type Relation

Modifier and Type	Method	Description
<O extends DBID> DistanceQuery<O>	AbstractDBIDRangeDistance.instantiate(Relation<O> database)
<T extends O> DistanceQuery<T>	Distance.instantiate(Relation<T> relation)	Instantiate with a database to get the actual distance query.
default <T extends O> DistanceQuery<T>	PrimitiveDistance.instantiate(Relation<T> relation)
<T extends DBID> DistanceQuery<T>	RandomStableDistance.instantiate(Relation<T> relation)
<T extends O> SharedNearestNeighborJaccardDistance.Instance<T>	SharedNearestNeighborJaccardDistance.instantiate(Relation<T> database)
default <T extends V> SpatialPrimitiveDistanceQuery<T>	SpatialPrimitiveDistance.instantiate(Relation<T> relation)

Constructors in elki.distance with parameters of type Relation

Constructor	Description
Instance(Relation<O> relation, Distance<? super O> parent)	Constructor.
Instance(Relation<O> relation, I index, F parent)	Constructor.
Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, SharedNearestNeighborJaccardDistance<T> parent)	Constructor.

Uses of Relation in elki.distance.adapter

Methods in elki.distance.adapter with parameters of type Relation

Modifier and Type	Method	Description
abstract <T extends O> DistanceQuery<T>	AbstractSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	ArccosSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	LinearSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	LnSimilarityAdapter.instantiate(Relation<T> database)

Constructors in elki.distance.adapter with parameters of type Relation

Constructor	Description
Instance(Relation<O> database, Distance<? super O> parent, SimilarityQuery<? super O> similarityQuery)	Constructor.
Instance(Relation<O> database, Distance<? super O> parent, SimilarityQuery<O> similarityQuery)	Constructor.
Instance(Relation<O> database, Distance<? super O> parent, SimilarityQuery<? super O> similarityQuery)	Constructor.
Instance(Relation<O> database, Distance<? super O> parent, SimilarityQuery<O> similarityQuery)	Constructor.

Uses of Relation in elki.distance.external

Methods in elki.distance.external with parameters of type Relation

Modifier and Type	Method	Description
<O extends DBID> DistanceQuery<O>	FileBasedSparseDoubleDistance.instantiate(Relation<O> relation)
<O extends DBID> DistanceQuery<O>	FileBasedSparseFloatDistance.instantiate(Relation<O> relation)

Uses of Relation in elki.distance.probabilistic

Methods in elki.distance.probabilistic with parameters of type Relation

Modifier and Type	Method	Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	HellingerDistance.instantiate(Relation<T> database)

Uses of Relation in elki.distance.set

Methods in elki.distance.set with parameters of type Relation

Modifier and Type	Method	Description
<T extends FeatureVector<?>> DistanceSimilarityQuery<T>	JaccardSimilarityDistance.instantiate(Relation<T> relation)

Uses of Relation in elki.distance.subspace

Methods in elki.distance.subspace with parameters of type Relation

Modifier and Type	Method	Description
<T extends NumberVector> SpatialPrimitiveDistanceQuery<T>	SubspaceLPNormDistance.instantiate(Relation<T> database)

Uses of Relation in elki.evaluation.clustering.internal

Methods in elki.evaluation.clustering.internal with parameters of type Relation

Modifier and Type	Method	Description
static int	SimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Compute centroids.
protected double[]	ConcordantPairsGammaTau.computeWithinDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
double	CIndex.evaluateClustering(Relation<? extends O> rel, DistanceQuery<O> dq, Clustering<?> c)	Evaluate a single clustering.
double	ClusterRadius.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	ConcordantPairsGammaTau.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	DaviesBouldinIndex.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	DBCV.evaluateClustering(Relation<O> relation, Clustering<?> cl)	Evaluate a single clustering.
double	PBMIndex.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	Silhouette.evaluateClustering(Relation<O> rel, DistanceQuery<O> dq, Clustering<?> c)	Evaluate a single clustering.
double	SimplifiedSilhouette.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	SquaredErrors.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	VarianceRatioCriterion.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
static int	VarianceRatioCriterion.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Update the global centroid.
protected void	CIndex.processSingleton(Cluster<?> cluster, Relation<? extends O> rel, DistanceQuery<O> dq, DoubleHeap maxDists, DoubleHeap minDists, int w)
double[]	DaviesBouldinIndex.withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Uses of Relation in elki.evaluation.similaritymatrix

Fields in elki.evaluation.similaritymatrix declared as Relation

Modifier and Type	Field	Description
(package private) Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.relation	The database

Methods in elki.evaluation.similaritymatrix that return Relation

Modifier and Type	Method	Description
Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.getRelation()	Get the relation

Methods in elki.evaluation.similaritymatrix with parameters of type Relation

Modifier and Type	Method	Description
private ComputeSimilarityMatrixImage.SimilarityMatrix	ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation, DBIDIter iter)	Compute the actual similarity image.

Constructors in elki.evaluation.similaritymatrix with parameters of type Relation

Constructor	Description
SimilarityMatrix(java.awt.image.RenderedImage img, Relation<?> relation, ArrayDBIDs ids)	Constructor

Uses of Relation in elki.index

Fields in elki.index declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	AbstractRefiningIndex.relation	The representation we are bound to.

Methods in elki.index with parameters of type Relation

Modifier and Type	Method	Description
Index	IndexFactory.instantiate(Relation<V> relation)	Sets the database in the distance function of this index (if existing).

Constructors in elki.index with parameters of type Relation

Constructor	Description
AbstractRefiningIndex(Relation<O> relation)	Constructor.

Uses of Relation in elki.index.distancematrix

Fields in elki.index.distancematrix declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	PrecomputedSimilarityMatrix.relation	The representation we are bound to.

Fields in elki.index.distancematrix with type parameters of type Relation

Modifier and Type	Field	Description
protected java.lang.ref.WeakReference<Relation<O>>	PrecomputedDistanceMatrix.refrelation	Data relation.

Methods in elki.index.distancematrix that return Relation

Modifier and Type	Method	Description
Relation<? extends O>	PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getRelation()
Relation<? extends O>	PrecomputedSimilarityMatrix.PrecomputedSimilarityQuery.getRelation()

Methods in elki.index.distancematrix with parameters of type Relation

Modifier and Type	Method	Description
PrecomputedDistanceMatrix<O>	PrecomputedDistanceMatrix.Factory.instantiate(Relation<O> relation)
PrecomputedSimilarityMatrix<O>	PrecomputedSimilarityMatrix.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.distancematrix with parameters of type Relation

Constructor	Description
PrecomputedDistanceMatrix(Relation<O> relation, DBIDRange range, Distance<? super O> distance)	Constructor.
PrecomputedSimilarityMatrix(Relation<O> relation, Similarity<? super O> similarityFunction)	Constructor.

Uses of Relation in elki.index.idistance

Methods in elki.index.idistance with parameters of type Relation

Modifier and Type	Method	Description
InMemoryIDistanceIndex<V>	InMemoryIDistanceIndex.Factory.instantiate(Relation<V> relation)

Constructors in elki.index.idistance with parameters of type Relation

Constructor	Description
InMemoryIDistanceIndex(Relation<O> relation, DistanceQuery<O> distance, KMedoidsInitialization<O> initialization, int numref)	Constructor.

Uses of Relation in elki.index.invertedlist

Fields in elki.index.invertedlist declared as Relation

Modifier and Type	Field	Description
protected Relation<V>	InMemoryInvertedIndex.relation	The representation we are bound to.

Methods in elki.index.invertedlist with parameters of type Relation

Modifier and Type	Method	Description
InMemoryInvertedIndex<V>	InMemoryInvertedIndex.Factory.instantiate(Relation<V> relation)

Constructors in elki.index.invertedlist with parameters of type Relation

Constructor	Description
InMemoryInvertedIndex(Relation<V> relation)	Constructor.

Uses of Relation in elki.index.laesa

Fields in elki.index.laesa declared as Relation

Modifier and Type	Field	Description
(package private) Relation<O>	LAESA.relation	Relation indexed.

Methods in elki.index.laesa with parameters of type Relation

Modifier and Type	Method	Description
LAESA<O>	LAESA.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.laesa with parameters of type Relation

Constructor	Description
LAESA(Relation<O> relation, Distance<? super O> distance, int m, int k, RandomFactory rng)	Constructor.

Uses of Relation in elki.index.lsh

Methods in elki.index.lsh with parameters of type Relation

Modifier and Type	Method	Description
InMemoryLSHIndex.Instance	InMemoryLSHIndex.instantiate(Relation<V> relation)

Constructors in elki.index.lsh with parameters of type Relation

Constructor	Description
Instance(Relation<V> relation, java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super V>> hashfunctions, int numberOfBuckets)	Constructor.

Uses of Relation in elki.index.lsh.hashfamilies

Methods in elki.index.lsh.hashfamilies with parameters of type Relation

Modifier and Type	Method	Description
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>	AbstractProjectedHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>	CosineHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super V>>	LocalitySensitiveHashFunctionFamily.generateHashFunctions(Relation<? extends V> relation, int l)	Generate hash functions for the given relation.

Uses of Relation in elki.index.preprocessed.fastoptics

Fields in elki.index.preprocessed.fastoptics declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	RandomProjectedNeighborsAndDensities.points	entire point set

Methods in elki.index.preprocessed.fastoptics with parameters of type Relation

Modifier and Type	Method	Description
void	RandomProjectedNeighborsAndDensities.computeSetsBounds(Relation<? extends NumberVector> points, int minSplitSize, DBIDs ptList)	Create random projections, project points and put points into sets of size about minSplitSize/2

Uses of Relation in elki.index.preprocessed.knn

Fields in elki.index.preprocessed.knn declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	AbstractMaterializeKNNPreprocessor.relation	The relation we are bound to.
protected Relation<O>	NaiveProjectedKNNPreprocessor.relation	The representation we are bound to.
protected Relation<O>	SpacefillingKNNPreprocessor.relation	The representation we are bound to.

Methods in elki.index.preprocessed.knn with parameters of type Relation

Modifier and Type	Method	Description
private MetricalIndexTree<O,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.getMetricalIndex(Relation<? extends O> relation)	Do some (limited) type checking, then cast the database into a spatial database.
protected AbstractRStarTree<?,SpatialEntry,?>	SpatialApproximationMaterializeKNNPreprocessor.getSpatialIndex(Relation<O> relation)
abstract AbstractMaterializeKNNPreprocessor<O>	AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
CachedDoubleDistanceKNNPreprocessor<O>	CachedDoubleDistanceKNNPreprocessor.Factory.instantiate(Relation<O> relation)
KNNJoinMaterializeKNNPreprocessor<O>	KNNJoinMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNAndRKNNPreprocessor<O>	MaterializeKNNAndRKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNPreprocessor<O>	MaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MetricalIndexApproximationMaterializeKNNPreprocessor<O,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
NaiveProjectedKNNPreprocessor<V>	NaiveProjectedKNNPreprocessor.Factory.instantiate(Relation<V> relation)
NNDescent<O>	NNDescent.Factory.instantiate(Relation<O> relation)
PartitionApproximationMaterializeKNNPreprocessor<O>	PartitionApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
RandomSampleKNNPreprocessor<O>	RandomSampleKNNPreprocessor.Factory.instantiate(Relation<O> relation)
SpacefillingKNNPreprocessor<V>	SpacefillingKNNPreprocessor.Factory.instantiate(Relation<V> relation)
SpacefillingMaterializeKNNPreprocessor<V>	SpacefillingMaterializeKNNPreprocessor.Factory.instantiate(Relation<V> relation)
SpatialApproximationMaterializeKNNPreprocessor<NumberVector>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation)

Constructors in elki.index.preprocessed.knn with parameters of type Relation

Constructor	Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceQuery<O> distanceQuery, int k)	Constructor.
AbstractMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k)	Constructor.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, java.nio.file.Path file)	Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, Distance<? super V> distance, int k)	Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k)	Constructor.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceQuery<O> distanceQuery, int k, boolean noopt)	Constructor with preprocessing step.
MaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k)	Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k)	Constructor
NaiveProjectedKNNPreprocessor(Relation<O> relation, double window, int projections, RandomProjectionFamily proj, java.util.Random random)	Constructor.
NNDescent(Relation<O> relation, Distance<? super O> distance, int k, RandomFactory rnd, double delta, double rho, boolean noInitialNeighbors, int iterations)	Constructor.
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, int partitions, RandomFactory rnd)	Constructor
RandomSampleKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, double share, RandomFactory rnd)	Constructor.
SpacefillingKNNPreprocessor(Relation<O> relation, java.util.List<? extends SpatialSorter> curvegen, double window, int variants, int odim, RandomProjectionFamily proj, java.util.Random random)	Constructor.
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, java.util.List<? extends SpatialSorter> curvegen, double window, int variants, java.util.Random random)	Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k)	Constructor

Uses of Relation in elki.index.preprocessed.snn

Fields in elki.index.preprocessed.snn declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	SharedNearestNeighborPreprocessor.relation	Relation to use.

Methods in elki.index.preprocessed.snn with parameters of type Relation

Modifier and Type	Method	Description
SharedNearestNeighborIndex<O>	SharedNearestNeighborIndex.Factory.instantiate(Relation<O> database)	Instantiate the index for a given database.
SharedNearestNeighborPreprocessor<O>	SharedNearestNeighborPreprocessor.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.preprocessed.snn with parameters of type Relation

Constructor	Description
SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, Distance<O> distance)	Constructor.

Uses of Relation in elki.index.projected

Fields in elki.index.projected declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends O>	ProjectedIndex.relation	The relation we predend to index.
(package private) Relation<I>	ProjectedIndex.view	The view that we really index.

Methods in elki.index.projected with parameters of type Relation

Modifier and Type	Method	Description
ProjectedIndex<O,O>	LatLngAsECEFIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,O>	LngLatAsECEFIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,I>	ProjectedIndex.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.projected with parameters of type Relation

Constructor	Description
LatLngAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine)	Constructor.
LngLatAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine)	Constructor.
ProjectedIndex(Relation<? extends O> relation, Projection<O,I> proj, Relation<I> view, Index inner, boolean norefine, double kmulti)	Constructor.

Uses of Relation in elki.index.tree.betula

Methods in elki.index.tree.betula with parameters of type Relation

Modifier and Type	Method	Description
CFTree<L>	CFTree.Factory.newTree(DBIDs ids, Relation<? extends NumberVector> relation, boolean storeIds)	Make a new tree.

Uses of Relation in elki.index.tree.metrical.covertree

Fields in elki.index.tree.metrical.covertree declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	AbstractCoverTree.relation	The representation we are bound to.

Methods in elki.index.tree.metrical.covertree with parameters of type Relation

Modifier and Type	Method	Description
CoverTree<O>	CoverTree.Factory.instantiate(Relation<O> relation)
SimplifiedCoverTree<O>	SimplifiedCoverTree.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.covertree with parameters of type Relation

Constructor	Description
AbstractCoverTree(Relation<O> relation, Distance<? super O> distance, double expansion, int truncate)	Constructor.
CoverTree(Relation<O> relation, Distance<? super O> distance, double expansion, int truncate)	Constructor.
CoverTree(Relation<O> relation, Distance<? super O> distance, int truncate)	Constructor.
SimplifiedCoverTree(Relation<O> relation, Distance<? super O> distance, double expansion, int truncate)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mktrees

Constructors in elki.index.tree.metrical.mtreevariants.mktrees with parameters of type Relation

Constructor	Description
AbstractMkTree(Relation<O> relation, PageFile<N> pagefile, S settings)	Constructor.
AbstractMkTreeUnified(Relation<O> relation, PageFile<N> pagefile, S settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mktrees.mkapp

Fields in elki.index.tree.metrical.mtreevariants.mktrees.mkapp declared as Relation

Modifier and Type	Field	Description
private Relation<O>	MkAppTreeIndex.relation	The relation indexed

Methods in elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type Relation

Modifier and Type	Method	Description
MkAppTreeIndex<O>	MkAppTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type Relation

Constructor	Description
MkAppTree(Relation<O> relation, PageFile<MkAppTreeNode<O>> pageFile, MkAppTreeSettings<O> settings)	Constructor.
MkAppTreeIndex(Relation<O> relation, PageFile<MkAppTreeNode<O>> pageFile, MkAppTreeSettings<O> settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mktrees.mkcop

Fields in elki.index.tree.metrical.mtreevariants.mktrees.mkcop declared as Relation

Modifier and Type	Field	Description
private Relation<O>	MkCoPTreeIndex.relation	Relation indexed

Methods in elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type Relation

Modifier and Type	Method	Description
MkCoPTreeIndex<O>	MkCopTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type Relation

Constructor	Description
MkCoPTree(Relation<O> relation, PageFile<MkCoPTreeNode<O>> pagefile, MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)	Constructor.
MkCoPTreeIndex(Relation<O> relation, PageFile<MkCoPTreeNode<O>> pageFile, MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mktrees.mkmax

Fields in elki.index.tree.metrical.mtreevariants.mktrees.mkmax declared as Relation

Modifier and Type	Field	Description
private Relation<O>	MkMaxTreeIndex.relation	Relation indexed.

Methods in elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type Relation

Modifier and Type	Method	Description
MkMaxTreeIndex<O>	MkMaxTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type Relation

Constructor	Description
MkMaxTree(Relation<O> relation, PageFile<MkMaxTreeNode<O>> pagefile, MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)	Constructor.
MkMaxTreeIndex(Relation<O> relation, PageFile<MkMaxTreeNode<O>> pagefile, MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mktrees.mktab

Fields in elki.index.tree.metrical.mtreevariants.mktrees.mktab declared as Relation

Modifier and Type	Field	Description
private Relation<O>	MkTabTreeIndex.relation	The relation indexed.

Methods in elki.index.tree.metrical.mtreevariants.mktrees.mktab with parameters of type Relation

Modifier and Type	Method	Description
MkTabTreeIndex<O>	MkTabTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.mtreevariants.mktrees.mktab with parameters of type Relation

Constructor	Description
MkTabTree(Relation<O> relation, PageFile<MkTabTreeNode<O>> pagefile, MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)	Constructor.
MkTabTreeIndex(Relation<O> relation, PageFile<MkTabTreeNode<O>> pagefile, MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.mtreevariants.mtree

Fields in elki.index.tree.metrical.mtreevariants.mtree declared as Relation

Modifier and Type	Field	Description
private Relation<O>	MTreeIndex.relation	The relation indexed.

Methods in elki.index.tree.metrical.mtreevariants.mtree with parameters of type Relation

Modifier and Type	Method	Description
MTreeIndex<O>	MTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.mtreevariants.mtree with parameters of type Relation

Constructor	Description
MTreeIndex(Relation<O> relation, PageFile<MTreeNode<O>> pagefile, MTreeSettings<O,MTreeNode<O>,MTreeEntry> settings)	Constructor.

Uses of Relation in elki.index.tree.metrical.vptree

Fields in elki.index.tree.metrical.vptree declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	GNAT.relation	The representation we are bound to.
protected Relation<O>	VPTree.relation	The representation we are bound to.

Methods in elki.index.tree.metrical.vptree with parameters of type Relation

Modifier and Type	Method	Description
GNAT<O>	GNAT.Factory.instantiate(Relation<O> relation)
VPTree<O>	VPTree.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.metrical.vptree with parameters of type Relation

Constructor	Description
GNAT(Relation<O> relation, Distance<? super O> distance, RandomFactory random, int numberVPs)	Constructor.
VPTree(Relation<O> relation, Distance<? super O> distance, int leafsize)	Constructor with default values, used by EmpiricalQueryOptimizer
VPTree(Relation<O> relation, Distance<? super O> distance, RandomFactory random, int sampleSize, int truncate)	Constructor.

Uses of Relation in elki.index.tree.spatial.kd

Classes in elki.index.tree.spatial.kd that implement Relation

Modifier and Type	Class	Description
private class	MemoryKDTree.CountingRelation	Proxy to count accesses.

Fields in elki.index.tree.spatial.kd declared as Relation

Modifier and Type	Field	Description
protected Relation<O>	MemoryKDTree.relation	The representation we are bound to.
protected Relation<O>	MinimalisticMemoryKDTree.relation	The representation we are bound to.
protected Relation<O>	SmallMemoryKDTree.relation	The representation we are bound to.

Methods in elki.index.tree.spatial.kd with parameters of type Relation

Modifier and Type	Method	Description
java.lang.Object	MemoryKDTree.buildTree(Relation<? extends NumberVector> relation, int left, int right, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d tree.
MemoryKDTree<O>	MemoryKDTree.Factory.instantiate(Relation<O> relation)
MinimalisticMemoryKDTree<O>	MinimalisticMemoryKDTree.Factory.instantiate(Relation<O> relation)
SmallMemoryKDTree<O>	SmallMemoryKDTree.Factory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.spatial.kd with parameters of type Relation

Constructor	Description
CountSortAccesses(Counter objaccess, Relation<? extends NumberVector> data)	Constructor.
MemoryKDTree(Relation<O> relation, int leafsize)	Constructor with default split (used by EmpiricalQueryOptimizer).
MemoryKDTree(Relation<O> relation, SplitStrategy split, int leafsize)	Constructor.
MinimalisticMemoryKDTree(Relation<O> relation, int leafsize)	Constructor.
SmallMemoryKDTree(Relation<O> relation, int leafsize)	Constructor.

Uses of Relation in elki.index.tree.spatial.kd.split

Methods in elki.index.tree.spatial.kd.split with parameters of type Relation

Modifier and Type	Method	Description
SplitStrategy.Info	BoundedMidpointSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	LeastOneDimSSQSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	LeastSSQSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MeanVarianceSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MedianSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MedianVarianceSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MidpointSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	SplitStrategy.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using midpoint splitting.
(package private) static double[]	SplitStrategy.Util.minmaxRange(int dims, Relation<? extends NumberVector> relation, DBIDArrayIter iter, int left, int right)	Find the minimum and maximum in each dimension of a range of values.
(package private) static int	SplitStrategy.Util.pivot(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int dim, int left, int right, double mid)	Pivot an interval.
(package private) static double[]	SplitStrategy.Util.sumvar(Relation<? extends NumberVector> relation, int dims, DBIDArrayMIter iter, int left, int right)	Compute the sum and sum-of-squares (for variance).

Uses of Relation in elki.index.tree.spatial.rstarvariants.deliclu

Fields in elki.index.tree.spatial.rstarvariants.deliclu declared as Relation

Modifier and Type	Field	Description
private Relation<O>	DeLiCluTreeIndex.relation	The relation we index.

Methods in elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Modifier and Type	Method	Description
DeLiCluTreeIndex<O>	DeLiCluTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Constructor	Description
DeLiCluTreeIndex(Relation<O> relation, PageFile<DeLiCluNode> pagefile, RTreeSettings settings)	Constructor.

Uses of Relation in elki.index.tree.spatial.rstarvariants.flat

Fields in elki.index.tree.spatial.rstarvariants.flat declared as Relation

Modifier and Type	Field	Description
private Relation<O>	FlatRStarTreeIndex.relation	The relation we index

Methods in elki.index.tree.spatial.rstarvariants.flat with parameters of type Relation

Modifier and Type	Method	Description
FlatRStarTreeIndex<O>	FlatRStarTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.spatial.rstarvariants.flat with parameters of type Relation

Constructor	Description
FlatRStarTreeIndex(Relation<O> relation, PageFile<FlatRStarTreeNode> pagefile, RTreeSettings settings)	Constructor.

Uses of Relation in elki.index.tree.spatial.rstarvariants.query

Fields in elki.index.tree.spatial.rstarvariants.query declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends O>	EuclideanRStarTreeDistancePrioritySearcher.relation	Relation we query.
protected Relation<? extends O>	RStarTreeDistancePrioritySearcher.relation	Relation we query.
protected Relation<? extends O>	RStarTreeKNNSearcher.relation	Relation we query.
protected Relation<? extends O>	RStarTreeRangeSearcher.relation	Relation we query.

Constructors in elki.index.tree.spatial.rstarvariants.query with parameters of type Relation

Constructor	Description
EuclideanRStarTreeDistancePrioritySearcher(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation)	Constructor.
EuclideanRStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation)	Constructor.
EuclideanRStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation)	Constructor.
RStarTreeDistancePrioritySearcher(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation, SpatialPrimitiveDistance<? super O> distance)	Constructor.
RStarTreeKNNSearcher(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation, SpatialPrimitiveDistance<? super O> distance)	Constructor.
RStarTreeRangeSearcher(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation, SpatialPrimitiveDistance<? super O> distance)	Constructor.

Uses of Relation in elki.index.tree.spatial.rstarvariants.rdknn

Fields in elki.index.tree.spatial.rstarvariants.rdknn declared as Relation

Modifier and Type	Field	Description
private Relation<O>	RdKNNTree.relation	The relation we query.

Methods in elki.index.tree.spatial.rstarvariants.rdknn with parameters of type Relation

Modifier and Type	Method	Description
RdKNNTree<O>	RdKNNTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.spatial.rstarvariants.rdknn with parameters of type Relation

Constructor	Description
RdKNNTree(Relation<O> relation, PageFile<RdKNNNode> pagefile, RdkNNSettings settings)	Constructor.

Uses of Relation in elki.index.tree.spatial.rstarvariants.rstar

Fields in elki.index.tree.spatial.rstarvariants.rstar declared as Relation

Modifier and Type	Field	Description
private Relation<O>	RStarTreeIndex.relation	Relation

Methods in elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Modifier and Type	Method	Description
RStarTreeIndex<O>	RStarTreeFactory.instantiate(Relation<O> relation)

Constructors in elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Constructor	Description
RStarTreeIndex(Relation<O> relation, PageFile<RStarTreeNode> pagefile, RTreeSettings settings)	Constructor.

Uses of Relation in elki.index.vafile

Methods in elki.index.vafile with parameters of type Relation

Modifier and Type	Method	Description
PartialVAFile<V>	PartialVAFile.Factory.instantiate(Relation<V> relation)
VAFile<V>	VAFile.Factory.instantiate(Relation<V> relation)
void	VAFile.setPartitions(Relation<V> relation)	Initialize the data set grid by computing quantiles.

Constructors in elki.index.vafile with parameters of type Relation

Constructor	Description
DAFile(Relation<? extends NumberVector> relation, int dimension, int partitions)	Constructor.
PartialVAFile(int pageSize, Relation<V> relation, int partitions)	Constructor.
VAFile(int pageSize, Relation<V> relation, int partitions)	Constructor.

Uses of Relation in elki.itemsetmining

Methods in elki.itemsetmining with parameters of type Relation

Modifier and Type	Method	Description
private FPGrowth.FPTree	FPGrowth.buildFPTree(Relation<BitVector> relation, int[] iidx, int items)	Build the actual FP-tree structure.
protected java.util.List<OneItemset>	APRIORI.buildFrequentOneItemsets(Relation<? extends SparseFeatureVector<?>> relation, int dim, int needed)	Build the 1-itemsets.
protected java.util.List<SparseItemset>	APRIORI.buildFrequentTwoItemsets(java.util.List<OneItemset> oneitems, Relation<BitVector> relation, int dim, int needed, DBIDs ids, ArrayModifiableDBIDs survivors)	Build the 2-itemsets.
private DBIDs[]	Eclat.buildIndex(Relation<BitVector> relation, int dim, int minsupp)
private int[]	FPGrowth.countItemSupport(Relation<BitVector> relation, int dim)	Count the support of each 1-item.
protected java.util.List<? extends Itemset>	APRIORI.frequentItemsets(java.util.List<? extends Itemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length)	Returns the frequent BitSets out of the given BitSets with respect to the given database.
protected java.util.List<SparseItemset>	APRIORI.frequentItemsetsSparse(java.util.List<SparseItemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length)	Returns the frequent BitSets out of the given BitSets with respect to the given database.
FrequentItemsetsResult	APRIORI.run(Relation<BitVector> relation)	Performs the APRIORI algorithm on the given database.
FrequentItemsetsResult	Eclat.run(Relation<BitVector> relation)	Run the Eclat algorithm
FrequentItemsetsResult	FPGrowth.run(Relation<BitVector> relation)	Run the FP-Growth algorithm

Uses of Relation in elki.math

Methods in elki.math with parameters of type Relation

Modifier and Type	Method	Description
static MeanVariance[]	MeanVariance.of(Relation<? extends NumberVector> relation)	Compute the variances of a relation.

Uses of Relation in elki.math.linearalgebra

Methods in elki.math.linearalgebra with parameters of type Relation

Modifier and Type	Method	Description
<F extends NumberVector> F	CovarianceMatrix.getMeanVector(Relation<? extends F> relation)	Get the mean as vector.
static Centroid	Centroid.make(Relation<? extends NumberVector> relation, DBIDs ids)	Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation)	Static Constructor from a full relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation, DBIDs ids)	Static Constructor from a full relation.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation)	Static Constructor from a relation.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation, DBIDs ids)	Static Constructor from a relation.

Uses of Relation in elki.math.linearalgebra.pca

Methods in elki.math.linearalgebra.pca with parameters of type Relation

Modifier and Type	Method	Description
PCAResult	AutotuningPCA.processIds(DBIDs ids, Relation<? extends NumberVector> database)
double[][]	CovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Compute covariance matrix for a collection of database IDs.
PCAResult	PCARunner.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Run PCA on a collection of database IDs.
double[][]	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)
double[][]	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Compute Covariance Matrix for a collection of database IDs.
double[][]	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)	Weighted Covariance Matrix for a set of IDs.
PCAResult	AutotuningPCA.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)
PCAResult	PCARunner.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)	Run PCA on a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database)	Compute covariance matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)	Compute covariance matrix for a QueryResult collection.
double[][]	WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)	Compute Covariance Matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processRelation(Relation<? extends NumberVector> relation)	Compute covariance matrix for a complete relation.

Uses of Relation in elki.math.scales

Methods in elki.math.scales with parameters of type Relation

Modifier and Type	Method	Description
static LinearScale[]	Scales.calcScales(Relation<? extends SpatialComparable> rel)	Compute a linear scale for each dimension.

Uses of Relation in elki.math.spacefillingcurves

Constructors in elki.math.spacefillingcurves with parameters of type Relation

Constructor	Description
ZCurveTransformer(Relation<? extends NumberVector> relation, DBIDs ids)	Constructor.

Uses of Relation in elki.math.statistics.intrinsicdimensionality

Methods in elki.math.statistics.intrinsicdimensionality with parameters of type Relation

Modifier and Type	Method	Description
protected double	LPCAEstimator.estimate(DBIDs ids, Relation<? extends NumberVector> relation)	Returns an ID estimate based on the specified filter for the given point DBID set and relation.

Uses of Relation in elki.outlier

Methods in elki.outlier with parameters of type Relation

Modifier and Type	Method	Description
private static void	COP.computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids)	Recompute the centroid of a set.
private double	GaussianUniformMixture.loglikelihoodNormal(DBIDs objids, SetDBIDs anomalous, CovarianceMatrix builder, Relation<? extends NumberVector> relation)	Computes the loglikelihood of all normal objects.
OutlierResult	COP.run(Relation<V> relation)	Process a single relation.
OutlierResult	DWOF.run(Relation<O> relation)	Performs the Generalized DWOF_SCORE algorithm on the given database by calling all the other methods in the proper order.
OutlierResult	GaussianModel.run(Relation<? extends NumberVector> relation)	Run the algorithm
OutlierResult	GaussianUniformMixture.run(Relation<? extends NumberVector> relation)	Run the algorithm
OutlierResult	OPTICSOF.run(Relation<O> relation)	Perform OPTICS-based outlier detection.
OutlierResult	SimpleCOP.run(Relation<V> relation)	Run Simple COP outlier detection.

Uses of Relation in elki.outlier.anglebased

Methods in elki.outlier.anglebased with parameters of type Relation

Modifier and Type	Method	Description
private void	FastABOD.fastABOD(Relation<V> relation, DBIDs ids, WritableDoubleDataStore abodvalues, DoubleMinMax minmaxabod)	Full kernel-based version.
private boolean	FastABOD.kNNABOD(Relation<V> relation, DBIDs ids, WritableDoubleDataStore abodvalues, DoubleMinMax minmaxabod)	Simpler kNN based, can use more indexing.
OutlierResult	ABOD.run(Relation<V> relation)	Run ABOD on the data set.
OutlierResult	FastABOD.run(Relation<V> relation)	Run Fast-ABOD on the data set.
OutlierResult	LBABOD.run(Relation<V> relation)	Run LB-ABOD on the data set.

Uses of Relation in elki.outlier.clustering

Methods in elki.outlier.clustering with parameters of type Relation

Modifier and Type	Method	Description
private void	CBLOF.computeCBLOFs(Relation<O> relation, WritableDoubleDataStore cblofs, DoubleMinMax cblofMinMax, java.util.List<? extends Cluster<MeanModel>> largeClusters, java.util.List<? extends Cluster<MeanModel>> smallClusters)	Compute the CBLOF scores for all the data.
private void	KMeansOutlierDetection.distanceScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Simple distance-based scoring function.
private int	CBLOF.getClusterBoundary(Relation<O> relation, java.util.List<? extends Cluster<MeanModel>> clusters)	Compute the boundary index separating the large cluster from the small cluster.
OutlierResult	CBLOF.run(Database database, Relation<O> relation)	Run CBLOF.
OutlierResult	DBSCANOutlierDetection.run(Database db, Relation<? extends NumberVector> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	EMOutlier.run(Relation<V> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	GLOSH.run(Database db, Relation<? extends NumberVector> relation)
OutlierResult	KMeansOutlierDetection.run(Relation<O> relation)	Run the outlier detection algorithm.
private void	KMeansOutlierDetection.singletonsScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Distance-based scoring that takes singletons into account.
private void	KMeansOutlierDetection.varianceScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Variance-based scoring function.

Uses of Relation in elki.outlier.density

Fields in elki.outlier.density declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	IsolationForest.ForestBuilder.relation	Data relation to use

Methods in elki.outlier.density with parameters of type Relation

Modifier and Type	Method	Description
private java.util.List<HySortOD.Hypercube>	HySortOD.getSortedHypercubes(Relation<? extends NumberVector> relation)	Create and sort hypercubes considering their coordinates.
OutlierResult	HySortOD.run(Database db, Relation<? extends NumberVector> relation)
OutlierResult	IsolationForest.run(Relation<? extends NumberVector> relation)	Run the isolation forest algorithm.

Constructors in elki.outlier.density with parameters of type Relation

Constructor	Description
ForestBuilder(Relation<? extends NumberVector> relation, int subsampleSize, java.util.Random random)	Constructor for the tree builder.

Uses of Relation in elki.outlier.distance

Fields in elki.outlier.distance declared as Relation

Modifier and Type	Field	Description
(package private) Relation<O>	HilOut.HilbertFeatures.relation	Relation indexed

Methods in elki.outlier.distance with parameters of type Relation

Modifier and Type	Method	Description
protected DoubleDBIDList	ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)	Computes for each object the distance to one reference point.
protected abstract DoubleDataStore	AbstractDBOutlier.computeOutlierScores(Relation<O> relation, double d)	computes an outlier score for each object of the database.
protected DoubleDataStore	DBOutlierDetection.computeOutlierScores(Relation<O> relation, double d)
protected DoubleDataStore	DBOutlierScore.computeOutlierScores(Relation<O> relation, double d)
OutlierResult	AbstractDBOutlier.run(Relation<O> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	HilOut.run(Relation<O> relation)	Run the HilOut algorithm.
OutlierResult	KNNDD.run(Relation<O> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	KNNOutlier.run(Relation<O> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	KNNSOS.run(Relation<O> relation)	Run the algorithm.
OutlierResult	KNNWeightOutlier.run(Relation<O> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	LocalIsolationCoefficient.run(Relation<O> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	ODIN.run(Relation<O> relation)	Run the ODIN algorithm
OutlierResult	ReferenceBasedOutlierDetection.run(Relation<? extends NumberVector> relation)	Run the algorithm on the given relation.
OutlierResult	SOS.run(Relation<O> relation)	Run the algorithm.

Constructors in elki.outlier.distance with parameters of type Relation

Constructor	Description
HilbertFeatures(Relation<O> relation, double[] min, double diameter)	Constructor.

Uses of Relation in elki.outlier.distance.parallel

Methods in elki.outlier.distance.parallel with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	ParallelKNNOutlier.run(Relation<O> relation)	Run the parallel kNN outlier detector.
OutlierResult	ParallelKNNWeightOutlier.run(Relation<O> relation)	Run the parallel kNN weight outlier detector.

Uses of Relation in elki.outlier.intrinsic

Methods in elki.outlier.intrinsic with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	IDOS.run(Relation<O> relation)	Run the algorithm
OutlierResult	ISOS.run(Relation<O> relation)	Run the algorithm.
OutlierResult	LID.run(Relation<O> relation)	Run the algorithm

Uses of Relation in elki.outlier.lof

Fields in elki.outlier.lof declared as Relation

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	ALOCI.ALOCIQuadTree.relation	Relation indexed.

Methods in elki.outlier.lof with parameters of type Relation

Modifier and Type	Method	Description
protected void	INFLO.computeINFLO(Relation<O> relation, ModifiableDBIDs pruned, KNNSearcher<DBIDRef> knnq, WritableDataStore<ModifiableDBIDs> rNNminuskNNs, WritableDoubleDataStore inflos, DoubleMinMax inflominmax)	Compute the final INFLO scores.
private void	INFLO.computeNeighborhoods(Relation<O> relation, DataStore<SetDBIDs> knns, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> rNNminuskNNs)	Compute the reverse kNN minus the kNN.
protected void	LoOP.computePDists(Relation<O> relation, KNNSearcher<DBIDRef> knn, WritableDoubleDataStore pdists)	Compute the probabilistic distances used by LoOP.
protected double	LoOP.computePLOFs(Relation<O> relation, KNNSearcher<DBIDRef> knn, WritableDoubleDataStore pdists, WritableDoubleDataStore plofs)	Compute the LOF values, using the pdist distances.
private int	KDEOS.dimensionality(Relation<O> rel)	Ugly hack to allow using this implementation without having a well-defined dimensionality.
protected void	KDEOS.estimateDensities(Relation<O> rel, KNNSearcher<DBIDRef> knnq, DBIDs ids, WritableDataStore<double[]> densities)	Perform the kernel density estimation step.
private Pair<Pair<KNNSearcher<DBIDRef>,KNNSearcher<DBIDRef>>,Pair<RKNNSearcher<DBIDRef>,RKNNSearcher<DBIDRef>>>	OnlineLOF.getKNNAndRkNNQueries(Relation<O> relation, StepProgress stepprog)	Get the kNN and rkNN queries for the algorithm.
OutlierResult	ALOCI.run(Relation<V> relation)	Run the algorithm.
OutlierResult	COF.run(Relation<O> relation)	Runs the COF algorithm on the given database.
OutlierResult	FlexibleLOF.run(Relation<O> relation)	Performs the Generalized LOF algorithm on the given database by calling FlexibleLOF.doRunInTime(elki.database.ids.DBIDs, elki.database.query.knn.KNNSearcher<elki.database.ids.DBIDRef>, elki.database.query.knn.KNNSearcher<elki.database.ids.DBIDRef>, elki.logging.progress.StepProgress).
OutlierResult	INFLO.run(Relation<O> relation)	Run the algorithm
OutlierResult	KDEOS.run(Relation<O> rel)	Run the KDEOS outlier detection algorithm.
OutlierResult	LDF.run(Relation<O> relation)	Run the naive kernel density LOF algorithm.
OutlierResult	LDOF.run(Relation<O> relation)	Run the algorithm
OutlierResult	LOCI.run(Relation<O> relation)	Run the algorithm
OutlierResult	LOF.run(Relation<O> relation)	Runs the LOF algorithm on the given database.
OutlierResult	LoOP.run(Relation<O> relation)	Performs the LoOP algorithm on the given database.
OutlierResult	OnlineLOF.run(Relation<O> relation)	Performs the Generalized LOF_SCORE algorithm on the given database by calling #doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to the preprocessors.
OutlierResult	SimpleKernelDensityLOF.run(Relation<O> relation)	Run the naive kernel density LOF algorithm.
OutlierResult	SimplifiedLOF.run(Relation<O> relation)	Run the Simple LOF algorithm.
OutlierResult	VarianceOfVolume.run(Relation<O> relation)	Runs the VOV algorithm on the given database.

Constructors in elki.outlier.lof with parameters of type Relation

Constructor	Description
ALOCIQuadTree(double[] min, double[] max, double[] shift, int nmin, Relation<? extends NumberVector> relation)	Constructor.

Uses of Relation in elki.outlier.lof.parallel

Methods in elki.outlier.lof.parallel with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	ParallelLOF.run(Relation<O> relation)	Run the LOF algorithm in parallel.
OutlierResult	ParallelSimplifiedLOF.run(Relation<O> relation)	Run the simplified LOF algorithm.

Uses of Relation in elki.outlier.meta

Methods in elki.outlier.meta with parameters of type Relation

Modifier and Type	Method	Description
private java.util.ArrayList<ArrayDBIDs>	HiCS.buildOneDimIndexes(Relation<? extends NumberVector> relation)	Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private void	HiCS.calculateContrast(Relation<? extends NumberVector> relation, HiCS.HiCSSubspace subspace, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)	Calculates the actual contrast of a given subspace.
private java.util.Set<HiCS.HiCSSubspace>	HiCS.calculateSubspaces(Relation<? extends NumberVector> relation, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)	Identifies high contrast subspaces in a given full-dimensional database.
OutlierResult	ExternalDoubleOutlierScore.run(Relation<?> relation)	Run the algorithm.
OutlierResult	FeatureBagging.run(Relation<NumberVector> relation)	Run the algorithm on a data set.
OutlierResult	HiCS.run(Relation<? extends NumberVector> relation)	Perform HiCS on a given database.

Uses of Relation in elki.outlier.spatial

Methods in elki.outlier.spatial with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Relation<V> relationx, Relation<? extends NumberVector> relationy)	Run the algorithm
OutlierResult	CTLuMeanMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes)	Run the algorithm
OutlierResult	CTLuMedianAlgorithm.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation)	Main method.
OutlierResult	CTLuMedianMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes)	Run the algorithm
OutlierResult	CTLuMoranScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation)	Main method.
OutlierResult	CTLuRandomWalkEC.run(Relation<O> spatial, Relation<? extends NumberVector> relation)	Run the algorithm.
OutlierResult	CTLuScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation)	Main method.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation)	Main method.
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	SOF.run(Database database, Relation<N> spatial, Relation<O> relation)	The main run method
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation)	Run the algorithm.
private Pair<DBIDVar,java.lang.Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector> relationy)	Run a single iteration of the GLS-SOD modeling step

Uses of Relation in elki.outlier.spatial.neighborhood

Methods in elki.outlier.spatial.neighborhood with parameters of type Relation

Modifier and Type	Method	Description
private DataStore<DBIDs>	ExtendedNeighborhood.Factory.extendNeighborhood(Database database, Relation<? extends O> relation)	Method to load the external neighbors.
NeighborSetPredicate	ExtendedNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
NeighborSetPredicate	ExternalNeighborhood.Factory.instantiate(Database database, Relation<?> relation)
NeighborSetPredicate	NeighborSetPredicate.Factory.instantiate(Database database, Relation<? extends O> relation)	Instantiation method.
NeighborSetPredicate	PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
private DataStore<DBIDs>	ExternalNeighborhood.Factory.loadNeighbors(Database database, Relation<?> relation)	Method to load the external neighbors.

Uses of Relation in elki.outlier.spatial.neighborhood.weighted

Methods in elki.outlier.spatial.neighborhood.weighted with parameters of type Relation

Modifier and Type	Method	Description
LinearWeightedExtendedNeighborhood	LinearWeightedExtendedNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
UnweightedNeighborhoodAdapter	UnweightedNeighborhoodAdapter.Factory.instantiate(Database database, Relation<? extends O> relation)
WeightedNeighborSetPredicate	WeightedNeighborSetPredicate.Factory.instantiate(Database database, Relation<? extends O> relation)	Instantiation method.

Uses of Relation in elki.outlier.subspace

Fields in elki.outlier.subspace declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	OUTRES.KernelDensityEstimator.relation	Relation to retrieve data from

Methods in elki.outlier.subspace with parameters of type Relation

Modifier and Type	Method	Description
protected java.util.ArrayList<java.util.ArrayList<DBIDs>>	AbstractAggarwalYuOutlier.buildRanges(Relation<? extends NumberVector> relation)	Grid discretization of the data: Each attribute of data is divided into phi equi-depth ranges. Each range contains a fraction f=1/phi of the records.
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation, double[] center, DBIDs neighborhood)	Compute the per-dimension variances for the given neighborhood and center.
private DBIDs	SOD.getNearestNeighbors(Relation<V> relation, SimilarityQuery<V> simQ, DBIDRef queryObject)	Get the k nearest neighbors in terms of the shared nearest neighbor distance.
OutlierResult	AggarwalYuEvolutionary.run(Relation<? extends NumberVector> relation)	Performs the evolutionary algorithm on the given database.
OutlierResult	AggarwalYuNaive.run(Relation<? extends NumberVector> relation)	Run the algorithm on the given relation.
OutlierResult	OUTRES.run(Relation<? extends NumberVector> relation)	Main loop for OUTRES
OutlierResult	SOD.run(Relation<V> relation)	Performs the SOD algorithm on the given database.

Constructors in elki.outlier.subspace with parameters of type Relation

Constructor	Description
EvolutionarySearch(Relation<? extends NumberVector> relation, java.util.ArrayList<java.util.ArrayList<DBIDs>> ranges, java.util.Random random)	Constructor.
KernelDensityEstimator(Relation<? extends NumberVector> relation, double eps)	Constructor.

Uses of Relation in elki.outlier.svm

Methods in elki.outlier.svm with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	LibSVMOneClassOutlierDetection.run(Relation<V> relation)	Run one-class SVM.
OutlierResult	OCSVM.run(Relation<V> relation)	Run one-class SVM.
OutlierResult	SVDD.run(Relation<V> relation)	Run one-class SVM.

Uses of Relation in elki.outlier.trivial

Methods in elki.outlier.trivial with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	ByLabelOutlier.run(Relation<?> relation)	Run the algorithm
OutlierResult	TrivialAllOutlier.run(Relation<?> relation)	Run the trivial algorithm.
OutlierResult	TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation)	Run the actual algorithm.
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels)	Run the algorithm
OutlierResult	TrivialNoOutlier.run(Relation<?> relation)	Run the trivial algorithm.

Uses of Relation in elki.projection

Methods in elki.projection that return Relation

Modifier and Type	Method	Description
Relation<DoubleVector>	SNE.autorun(Database database)
Relation<DoubleVector>	TSNE.autorun(Database database)
Relation<DoubleVector>	BarnesHutTSNE.run(Database database, Relation<O> relation)
Relation<DoubleVector>	SNE.run(Relation<O> relation)	Perform SNE projection.
Relation<DoubleVector>	TSNE.run(Relation<O> relation)	Perform tSNE projection.

Methods in elki.projection with parameters of type Relation

Modifier and Type	Method	Description
<T extends O> AffinityMatrix	AffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)	Compute the affinity matrix.
<T extends O> AffinityMatrix	GaussianAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	IntrinsicNearestNeighborAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	NearestNeighborAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	PerplexityAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
protected void	AbstractProjectionAlgorithm.removePreviousRelation(Relation<?> relation)	Remove the previous relation.
Relation<DoubleVector>	BarnesHutTSNE.run(Database database, Relation<O> relation)
Relation<DoubleVector>	SNE.run(Relation<O> relation)	Perform SNE projection.
Relation<DoubleVector>	TSNE.run(Relation<O> relation)	Perform tSNE projection.

Uses of Relation in elki.result

Methods in elki.result that return types with arguments of type Relation

Modifier and Type	Method	Description
static java.util.List<Relation<?>>	ResultUtil.getRelations(java.lang.Object r)	Collect all Annotation results from a Result

Methods in elki.result with parameters of type Relation

Modifier and Type	Method	Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords)	Recursively step through the clusters to build the hulls.
static SamplingResult	SamplingResult.getSamplingResult(Relation<?> rel)	Get the sampling result attached to a relation
static ScalesResult	ScalesResult.getScalesResult(Relation<? extends SpatialComparable> rel)	Get (or create) a scales result for a relation.

Method parameters in elki.result with type arguments of type Relation

Modifier and Type	Method	Description
private java.lang.StringBuilder	KMLOutputHandler.makeDescription(java.util.Collection<Relation<?>> relations, DBIDRef id)	Make an HTML description.

Constructors in elki.result with parameters of type Relation

Constructor	Description
SamplingResult(Relation<?> rel)	Constructor.
ScalesResult(Relation<? extends SpatialComparable> relation)	Constructor.

Uses of Relation in elki.result.textwriter

Method parameters in elki.result.textwriter with type arguments of type Relation

Modifier and Type	Method	Description
private void	TextWriter.printObject(TextWriterStream out, Database db, DBIDRef objID, java.util.List<Relation<?>> ra)
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, java.util.List<Relation<?>> ra, NamingScheme naming)
private void	TextWriter.writeOrderingResult(Database db, StreamFactory streamOpener, OrderingResult or, java.util.List<Relation<?>> ra)

Uses of Relation in elki.similarity

Fields in elki.similarity declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends DBID>	AbstractDBIDSimilarity.database	The database we work on
protected Relation<O>	AbstractIndexBasedSimilarity.Instance.relation	Relation to query.

Methods in elki.similarity that return Relation

Modifier and Type	Method	Description
Relation<? extends O>	AbstractIndexBasedSimilarity.Instance.getRelation()

Methods in elki.similarity with parameters of type Relation

Modifier and Type	Method	Description
abstract <T extends O> AbstractIndexBasedSimilarity.Instance<T,?>	AbstractIndexBasedSimilarity.instantiate(Relation<T> database)
<T extends O> FractionalSharedNearestNeighborSimilarity.Instance<T>	FractionalSharedNearestNeighborSimilarity.instantiate(Relation<T> database)
<T extends O> IndexBasedSimilarity.Instance<T,?>	IndexBasedSimilarity.instantiate(Relation<T> database)	Preprocess the database to get the actual distance function.
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	Kulczynski1Similarity.instantiate(Relation<T> database)
default <T extends O> SimilarityQuery<T>	PrimitiveSimilarity.instantiate(Relation<T> relation)
<T extends O> SharedNearestNeighborSimilarity.Instance<T>	SharedNearestNeighborSimilarity.instantiate(Relation<T> database)
<T extends O> SimilarityQuery<T>	Similarity.instantiate(Relation<T> relation)	Instantiate with a representation to get the actual similarity query.

Constructors in elki.similarity with parameters of type Relation

Constructor	Description
AbstractDBIDSimilarity(Relation<? extends DBID> database)	Constructor.
Instance(Relation<O> relation, I index)	Constructor.
Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, FractionalSharedNearestNeighborSimilarity<? super T> similarityFunction)	Constructor.
Instance(Relation<O> database, SharedNearestNeighborIndex<O> preprocessor, SharedNearestNeighborSimilarity<? super O> similarityFunction)	Constructor.

Uses of Relation in elki.similarity.cluster

Methods in elki.similarity.cluster with parameters of type Relation

Modifier and Type	Method	Description
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringAdjustedRandIndexSimilarity.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringBCubedF1Similarity.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringDistanceSimilarity.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringFowlkesMallowsSimilarity.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringRandIndexSimilarity.instantiate(Relation<T> relation)
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterIntersectionSimilarity.instantiate(Relation<T> relation)
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterJaccardSimilarity.instantiate(Relation<T> relation)

Uses of Relation in elki.similarity.kernel

Methods in elki.similarity.kernel with parameters of type Relation

Modifier and Type	Method	Description
<T extends NumberVector> DistanceSimilarityQuery<T>	PolynomialKernel.instantiate(Relation<T> database)

Constructors in elki.similarity.kernel with parameters of type Relation

Constructor	Description
KernelMatrix(SimilarityQuery<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids)	Provides a new kernel matrix.
KernelMatrix(PrimitiveSimilarity<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids)	Provides a new kernel matrix.

Uses of Relation in elki.timeseries

Methods in elki.timeseries with parameters of type Relation

Modifier and Type	Method	Description
ChangePoints	OfflineChangePointDetectionAlgorithm.Instance.run(Relation<DoubleVector> relation)	Run the change point detection algorithm on a data relation.
ChangePoints	OfflineChangePointDetectionAlgorithm.run(Relation<DoubleVector> relation)	Executes multiple change point detection for given relation
ChangePoints	SigniTrendChangeDetection.Instance.run(Relation<NumberVector> relation)	Process a relation.
ChangePoints	SigniTrendChangeDetection.run(Relation<NumberVector> relation)	Executes Signi-Trend for given relation

Uses of Relation in elki.utilities.referencepoints

Methods in elki.utilities.referencepoints with parameters of type Relation

Modifier and Type	Method	Description
java.util.Collection<? extends NumberVector>	AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	GridBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomGeneratedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomSampleReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	ReferencePointsHeuristic.getReferencePoints(Relation<? extends NumberVector> db)	Get the reference points for the given database.
java.util.Collection<? extends NumberVector>	StarBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)

Uses of Relation in elki.visualization

Fields in elki.visualization declared as Relation

Modifier and Type	Field	Description
private Relation<?>	VisualizationTask.relation	The main representation

Methods in elki.visualization with type parameters of type Relation

Modifier and Type	Method	Description
<R extends Relation<?>> R	VisualizationTask.getRelation()

Constructors in elki.visualization with parameters of type Relation

Constructor	Description
VisualizationTask(VisFactory factory, java.lang.String name, java.lang.Object result, Relation<?> relation)	Visualization task.

Uses of Relation in elki.visualization.parallel3d

Fields in elki.visualization.parallel3d declared as Relation

Modifier and Type	Field	Description
(package private) Relation<? extends O>	OpenGL3DParallelCoordinates.Instance.Shared.rel	Relation to visualize

Constructors in elki.visualization.parallel3d with parameters of type Relation

Constructor	Description
Instance(Relation<? extends O> rel, ProjectionParallel proj, OpenGL3DParallelCoordinates.Settings<O> settings, StylingPolicy stylepol, StyleLibrary stylelib)	Constructor.

Uses of Relation in elki.visualization.parallel3d.layout

Methods in elki.visualization.parallel3d.layout with parameters of type Relation

Modifier and Type	Method	Description
static double[]	AbstractLayout3DPC.computeSimilarityMatrix(Dependence sim, Relation<? extends NumberVector> rel)	Compute a column-wise dependency matrix for the given relation.
Layout	AbstractLayout3DPC.layout(Relation<? extends NumberVector> rel)
Layout	Layouter3DPC.layout(Relation<? extends V> rel)	Run the layouting algorithm.

Uses of Relation in elki.visualization.projector

Fields in elki.visualization.projector declared as Relation

Modifier and Type	Field	Description
(package private) Relation<V>	HistogramProjector.rel	Relation we project.
(package private) Relation<V>	ParallelPlotProjector.rel	Relation we project.
(package private) Relation<V>	ScatterPlotProjector.rel	Relation we project.

Methods in elki.visualization.projector that return Relation

Modifier and Type	Method	Description
Relation<V>	HistogramProjector.getRelation()	Get the relation we project.
Relation<V>	ParallelPlotProjector.getRelation()	The relation we project.
Relation<V>	ScatterPlotProjector.getRelation()	The relation we project.

Methods in elki.visualization.projector with parameters of type Relation

Modifier and Type	Method	Description
private int	ParallelPlotFactory.dimensionality(Relation<?> rel)
private int	ScatterPlotFactory.dimensionality(Relation<?> rel)

Constructors in elki.visualization.projector with parameters of type Relation

Constructor	Description
HistogramProjector(Relation<V> rel, int maxdim)	Constructor.
ParallelPlotProjector(Relation<V> rel)	Constructor.
ScatterPlotProjector(Relation<V> rel, int maxdim)	Constructor.

Uses of Relation in elki.visualization.visualizers.histogram

Fields in elki.visualization.visualizers.histogram declared as Relation

Modifier and Type	Field	Description
private Relation<NV>	ColoredHistogramVisualizer.Instance.relation	The database we visualize

Uses of Relation in elki.visualization.visualizers.parallel

Fields in elki.visualization.visualizers.parallel declared as Relation

Modifier and Type	Field	Description
protected Relation<NV>	AbstractParallelVisualization.relation	The representation we visualize

Uses of Relation in elki.visualization.visualizers.scatterplot

Fields in elki.visualization.visualizers.scatterplot declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	AbstractScatterplotVisualization.rel	The representation we visualize
protected Relation<PolygonsObject>	PolygonVisualization.Instance.rep	The representation we visualize
private Relation<? extends java.lang.Number>	TooltipScoreVisualization.Instance.result	Number value to visualize
private Relation<?>	TooltipStringVisualization.Instance.result	Number value to visualize

Methods in elki.visualization.visualizers.scatterplot with parameters of type Relation

Modifier and Type	Method	Description
private void	TooltipScoreVisualization.addTooltips(java.lang.String nam, Relation<?> val, VisualizerContext context, ScatterPlotProjector<?> p, Relation<?> rel)	Add tooltips.
private void	TooltipStringVisualization.addTooltips(java.lang.String name, Relation<?> rel, VisualizerContext context, Relation<?> rep, ScatterPlotProjector<?> p)

Uses of Relation in elki.visualization.visualizers.scatterplot.index

Methods in elki.visualization.visualizers.scatterplot.index with parameters of type Relation

Modifier and Type	Method	Description
static boolean	TreeSphereVisualization.canVisualize(Relation<?> rel, AbstractMTree<?,?,?,?> tree)	Test for a visualizable index in the context's database.

Uses of Relation in elki.visualization.visualizers.scatterplot.outlier

Fields in elki.visualization.visualizers.scatterplot.outlier declared as Relation

Modifier and Type	Field	Description
protected Relation<double[]>	COPVectorVisualization.Instance.result	The outlier result to visualize

Uses of Relation in elki.visualization.visualizers.scatterplot.uncertain

Fields in elki.visualization.visualizers.scatterplot.uncertain declared as Relation

Modifier and Type	Field	Description
protected Relation<? extends UncertainObject>	UncertainBoundingBoxVisualization.Instance.rel	The representation we visualize
protected Relation<? extends UncertainObject>	UncertainSamplesVisualization.Instance.rel	The representation we visualize

Uses of Relation in tutorial.clustering

Methods in tutorial.clustering with parameters of type Relation

Modifier and Type	Method	Description
protected WritableDataStore<SameSizeKMeans.Meta>	SameSizeKMeans.initializeMeta(Relation<V> relation, double[][] means)	Initialize the metadata storage.
protected double[][]	SameSizeKMeans.refineResult(Relation<V> relation, double[][] means, java.util.List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeans.Meta> metas, ArrayModifiableDBIDs tids)	Perform k-means style iterations to improve the clustering result.
Clustering<SimplePrototypeModel<DBID>>	CFSFDP.run(Relation<O> relation)	Perform CFSFDP clustering.
Clustering<Model>	NaiveAgglomerativeHierarchicalClustering1.run(Relation<O> relation)	Perform HAC
Clustering<Model>	NaiveAgglomerativeHierarchicalClustering2.run(Relation<O> relation)	Perform HAC
Clustering<Model>	NaiveAgglomerativeHierarchicalClustering3.run(Relation<O> relation)	Perform HAC
ClusterMergeHistory	NaiveAgglomerativeHierarchicalClustering4.run(Relation<O> relation)	Run the algorithm
Clustering<MeanModel>	SameSizeKMeans.run(Relation<V> relation)	Run k-means with cluster size constraints.
protected void	SameSizeKMeans.updateDistances(Relation<V> relation, double[][] means, WritableDataStore<SameSizeKMeans.Meta> metas, NumberVectorDistance<? super V> df)	Compute the distances of each object to all means.

Uses of Relation in tutorial.outlier

Methods in tutorial.outlier with parameters of type Relation

Modifier and Type	Method	Description
OutlierResult	DistanceStddevOutlier.run(Relation<O> relation)	Run the outlier detection algorithm
OutlierResult	ODIN.run(Relation<O> relation)	Run the ODIN algorithm