Uses of Class
elki.data.Clustering

Packages that use Clustering

Package	Description
elki.clustering	Clustering algorithms.
elki.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.parallel	Parallel versions of Generalized DBSCAN.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.kcenter	K-center clustering.
elki.clustering.kmeans	K-means clustering and variations.
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.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.datasource.parser	Parsers for different file formats and data types.
elki.evaluation.clustering	Evaluation of clustering results.
elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
elki.outlier.clustering	Clustering based outlier detection.
elki.result	Result types, representation and handling.
elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...).
elki.result.textwriter.naming	Naming schemes for clusters (for output when an algorithm does not generate cluster names).
elki.similarity.cluster	Similarity measures for comparing clusters.
elki.visualization	Visualization package of ELKI.
elki.visualization.opticsplot	Code for drawing OPTICS plots.
elki.visualization.projections	Visualization projections.
elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
elki.visualization.style	Style management for ELKI visualizations.
elki.visualization.visualizers.actions	Action-only "visualizers" that only produce menu entries.
elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots.
elki.visualization.visualizers.visunproj	Visualizers that do not use a particular projection.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of Clustering in elki.clustering

Classes in elki.clustering with type parameters of type Clustering

Modifier and Type	Class	Description
class	AbstractProjectedClustering<R extends Clustering<?>>	Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
interface	ClusteringAlgorithm<C extends Clustering<? extends Model>>	Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.

Methods in elki.clustering that return Clustering

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 Clustering in elki.clustering.affinitypropagation

Methods in elki.clustering.affinitypropagation that return Clustering

Modifier and Type	Method	Description
private Clustering<MedoidModel>	AffinityPropagation.buildResult(ArrayDBIDs ids, int[] assignment)	Build the clustering result.
Clustering<MedoidModel>	AffinityPropagation.run(Relation<O> relation)	Perform affinity propagation clustering.

Uses of Clustering in elki.clustering.biclustering

Methods in elki.clustering.biclustering that return Clustering

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

Uses of Clustering in elki.clustering.correlation

Methods in elki.clustering.correlation that return Clustering

Modifier and Type	Method	Description
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.
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.
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.

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

Modifier and Type	Method	Description
private void	ERiC.buildHierarchy(Clustering<CorrelationModel> clustering, java.util.List<java.util.List<Cluster<CorrelationModel>>> clusterMap, ERiCNeighborPredicate.Instance npred)
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.

Uses of Clustering in elki.clustering.dbscan

Methods in elki.clustering.dbscan that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	GeneralizedDBSCAN.autorun(Database database)
protected Clustering<Model>	GriDBSCAN.Instance.buildResult(DBIDs ids, int clusterid)	Assemble the clustering result.
Clustering<Model>	DBSCAN.run(Relation<O> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GeneralizedDBSCAN.Instance.run()	Run the actual GDBSCAN algorithm.
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

Uses of Clustering in elki.clustering.dbscan.parallel

Methods in elki.clustering.dbscan.parallel that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	ParallelGeneralizedDBSCAN.autorun(Database database)
Clustering<Model>	ParallelGeneralizedDBSCAN.Instance.run()	Run the parallel GDBSCAN algorithm.

Uses of Clustering in elki.clustering.em

Methods in elki.clustering.em that return Clustering

Modifier and Type	Method	Description
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

Uses of Clustering in elki.clustering.hierarchical.birch

Methods in elki.clustering.hierarchical.birch that return Clustering

Modifier and Type	Method	Description
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<KMeansModel>	BIRCHLloydKMeans.run(Relation<NumberVector> relation)	Run the clustering algorithm.

Uses of Clustering in elki.clustering.hierarchical.extraction

Methods in elki.clustering.hierarchical.extraction that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	ClustersWithNoiseExtraction.autorun(Database database)
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.autorun(Database database)
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.autorun(Database database)
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildFlat(int split, FiniteProgress progress)	Build a flat clustering.
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildHierarchical(int split, FiniteProgress progress)	Build a hierarchical clustering.
Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.extractClusters()	Extract all clusters from the pi-lambda-representation.
abstract Clustering<DendrogramModel>	AbstractCutDendrogram.run(ClusterMergeHistory pointerresult)	Process a pointer hierarchy result.
Clustering<DendrogramModel>	AbstractCutDendrogram.run(Database database)	Run the algorithms on a database.
Clustering<Model>	ClustersWithNoiseExtraction.Instance.run()	Extract all clusters from the pi-lambda-representation.
Clustering<Model>	ClustersWithNoiseExtraction.run(ClusterMergeHistory merges)	Process an existing result.
Clustering<DendrogramModel>	CutDendrogramByHeight.run(ClusterMergeHistory merges)
Clustering<DendrogramModel>	CutDendrogramByNumberOfClusters.run(ClusterMergeHistory merges)
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.Instance.run()	Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.run(ClusterMergeHistory merges)	Process an existing result.
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.Instance.run()	Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.run(ClusterMergeHistory merges)	Process an existing result.

Methods in elki.clustering.hierarchical.extraction with parameters of type Clustering

Modifier and Type	Method	Description
private double	HDBSCANHierarchyExtraction.Instance.collectChildren(HDBSCANHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, WritableDoubleDataStore glosh, HDBSCANHierarchyExtraction.TempCluster cur, Cluster<DendrogramModel> clus, boolean flatten)	Recursive flattening of clusters.
private double	HDBSCANHierarchyExtraction.Instance.finalizeCluster(HDBSCANHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, WritableDoubleDataStore glosh, Cluster<DendrogramModel> parent, boolean flatten)	Make the cluster for the given object
protected Cluster<DendrogramModel>	SimplifiedHierarchyExtraction.Instance.toCluster(SimplifiedHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering)	Make the cluster for the given object

Uses of Clustering in elki.clustering.kcenter

Methods in elki.clustering.kcenter that return Clustering

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

Uses of Clustering in elki.clustering.kmeans

Methods in elki.clustering.kmeans that return Clustering

Modifier and Type	Method	Description
protected Clustering<MeanModel>	KMediansLloyd.Instance.buildMediansResult()
Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult()	Build a standard k-means result, with known cluster variance sums.
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 Clustering<KMeansModel>	KMeansMinusMinus.Instance.buildResultWithNoise()
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)

Uses of Clustering in elki.clustering.kmeans.parallel

Methods in elki.clustering.kmeans.parallel that return Clustering

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

Uses of Clustering in elki.clustering.kmeans.quality

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

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.
static int	AbstractKMeansQualityMeasure.numPoints(Clustering<? extends MeanModel> clustering)	Compute the number of points in a given set of clusters (which may be less than the complete data set for X-means!)
<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)

Uses of Clustering in elki.clustering.kmeans.spherical

Methods in elki.clustering.kmeans.spherical that return Clustering

Modifier and Type	Method	Description
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)

Uses of Clustering in elki.clustering.kmedoids

Methods in elki.clustering.kmedoids that return Clustering

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)
protected static Clustering<MedoidModel>	PAM.wrapResult(DBIDs ids, WritableIntegerDataStore assignment, ArrayModifiableDBIDs medoids, java.lang.String name)	Wrap the clustering result.

Uses of Clustering in elki.clustering.meta

Methods in elki.clustering.meta that return Clustering

Modifier and Type	Method	Description
Clustering<? extends Model>	ExternalClustering.autorun(Database database)	Run the algorithm.

Uses of Clustering in elki.clustering.onedimensional

Methods in elki.clustering.onedimensional that return Clustering

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

Uses of Clustering in elki.clustering.optics

Fields in elki.clustering.optics declared as Clustering

Modifier and Type	Field	Description
(package private) Clustering<OPTICSModel>	OPTICSXi.ClusterHierarchyBuilder.clustering	ELKI clustering object

Methods in elki.clustering.optics that return Clustering

Modifier and Type	Method	Description
Clustering<OPTICSModel>	OPTICSXi.autorun(Database database)
private Clustering<OPTICSModel>	OPTICSXi.ClusterHierarchyBuilder.build(ClusterOrder clusterOrder, DBIDArrayIter iter)	Build the main clustering result.
private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrder clusterOrderResult, double ixi, int minpts)	Extract clusters from a cluster order result.
Clustering<OPTICSModel>	OPTICSXi.run(ClusterOrder clusterOrder)	Process the cluster order of an OPTICS clustering.

Uses of Clustering in elki.clustering.silhouette

Methods in elki.clustering.silhouette that return Clustering

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 Clustering in elki.clustering.subspace

Methods in elki.clustering.subspace that return Clustering

Modifier and Type	Method	Description
private Clustering<SubspaceModel>	DiSH.computeClusters(Relation<? extends NumberVector> database, DiSH.DiSHClusterOrder clusterOrder)	Computes the hierarchical clusters according to the cluster order.
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.
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.

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

Modifier and Type	Method	Description
private void	DiSH.buildHierarchy(Relation<? extends NumberVector> database, Clustering<SubspaceModel> clustering, java.util.List<Cluster<SubspaceModel>> clusters, int dimensionality)	Builds the cluster hierarchy.

Uses of Clustering in elki.clustering.svm

Methods in elki.clustering.svm that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	SupportVectorClustering.run(Relation<NumberVector> relation)	perform clustering

Uses of Clustering in elki.clustering.trivial

Subclasses of Clustering in elki.clustering.trivial

Modifier and Type	Class	Description
class	ReferenceClustering<M extends Model>	Reference clustering.

Methods in elki.clustering.trivial that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	ByLabelClustering.autorun(Database database)
Clustering<Model>	ByLabelHierarchicalClustering.autorun(Database database)
Clustering<Model>	ByLabelOrAllInOneClustering.autorun(Database database)
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.

Uses of Clustering in elki.clustering.uncertain

Classes in elki.clustering.uncertain with type parameters of type Clustering

Modifier and Type	Class	Description
class	CenterOfMassMetaClustering<C extends Clustering<?>>	Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.
static class	CenterOfMassMetaClustering.Par<C extends Clustering<?>>	Parameterization class.

Methods in elki.clustering.uncertain that return Clustering

Modifier and Type	Method	Description
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.
protected Clustering<?>	RepresentativeUncertainClustering.runClusteringAlgorithm(java.lang.Object parent, DBIDs ids, DataStore<DoubleVector> store, int dim, java.lang.String title)	Run a clustering algorithm on a single instance.

Uses of Clustering in elki.data

Fields in elki.data with type parameters of type Clustering

Modifier and Type	Field	Description
static SimpleTypeInformation<Clustering<?>>	Clustering.TYPE	Type information, for relation matching.

Methods in elki.data that return types with arguments of type Clustering

Modifier and Type	Method	Description
static java.util.List<Clustering<? extends Model>>	Clustering.getClusteringResults(java.lang.Object r)	Collect all clustering results from a Result

Uses of Clustering in elki.datasource.parser

Fields in elki.datasource.parser declared as Clustering

Modifier and Type	Field	Description
(package private) Clustering<Model>	ClusteringVectorParser.curclu	Current clustering.

Uses of Clustering in elki.evaluation.clustering

Methods in elki.evaluation.clustering with parameters of type Clustering

Modifier and Type	Method	Description
protected void	EvaluateClustering.evaluteResult(Database db, Clustering<?> c, Clustering<?> refc)	Evaluate a clustering result.
private boolean	EvaluateClustering.isReferenceResult(Clustering<?> t)	Test if a clustering result is a valid reference result.
static <C extends Model> void	LogClusterSizes.logClusterSizes(Clustering<C> c)	Log the cluster sizes of a clustering.

Constructors in elki.evaluation.clustering with parameters of type Clustering

Constructor	Description
ClusterContingencyTable(boolean selfPairing, boolean breakNoiseClusters, Clustering<?> result1, Clustering<?> result2)	Constructor.

Uses of Clustering in elki.evaluation.clustering.internal

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

Modifier and Type	Method	Description
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.

Uses of Clustering in elki.evaluation.clustering.pairsegments

Fields in elki.evaluation.clustering.pairsegments with type parameters of type Clustering

Modifier and Type	Field	Description
private java.util.List<Clustering<?>>	Segments.clusterings	Clusterings

Constructor parameters in elki.evaluation.clustering.pairsegments with type arguments of type Clustering

Constructor	Description
Segments(java.util.List<Clustering<?>> clusterings)	Initialize segments.

Uses of Clustering in elki.evaluation.outlier

Methods in elki.evaluation.outlier that return Clustering

Modifier and Type	Method	Description
private Clustering<Model>	OutlierThresholdClustering.split(OutlierResult or)

Uses of Clustering in elki.outlier.clustering

Fields in elki.outlier.clustering with type parameters of type Clustering

Modifier and Type	Field	Description
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.clusteringAlgorithm	The clustering algorithm to use.
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.Par.clusteringAlgorithm	The clustering algorithm to use.

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

Modifier and Type	Method	Description
private void	KMeansOutlierDetection.distanceScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Simple distance-based scoring function.
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.

Constructor parameters in elki.outlier.clustering with type arguments of type Clustering

Constructor	Description
CBLOF(NumberVectorDistance<? super O> distance, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta)	Constructor.

Uses of Clustering in elki.result

Methods in elki.result with parameters of type Clustering

Modifier and Type	Method	Description
protected void	ClusteringVectorDumper.dumpClusteringOutput(java.lang.Appendable writer, Clustering<?> c)	Dump a single clustering result.
private void	KMLOutputHandler.writeClusteringResult(javax.xml.stream.XMLStreamWriter xmlw, Clustering<Model> clustering, Database database)

Uses of Clustering in elki.result.textwriter

Methods in elki.result.textwriter with parameters of type Clustering

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

Uses of Clustering in elki.result.textwriter.naming

Fields in elki.result.textwriter.naming declared as Clustering

Modifier and Type	Field	Description
private Clustering<?>	SimpleEnumeratingScheme.clustering	Clustering this scheme is applied to.

Constructors in elki.result.textwriter.naming with parameters of type Clustering

Constructor	Description
SimpleEnumeratingScheme(Clustering<?> clustering)	Constructor.

Uses of Clustering in elki.similarity.cluster

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

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)

Methods in elki.similarity.cluster that return types with arguments of type Clustering

Modifier and Type	Method	Description
SimpleTypeInformation<? super Clustering<?>>	ClusteringAdjustedRandIndexSimilarity.getInputTypeRestriction()
SimpleTypeInformation<? super Clustering<?>>	ClusteringBCubedF1Similarity.getInputTypeRestriction()
SimpleTypeInformation<? super Clustering<?>>	ClusteringFowlkesMallowsSimilarity.getInputTypeRestriction()
SimpleTypeInformation<? super Clustering<?>>	ClusteringRandIndexSimilarity.getInputTypeRestriction()

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

Modifier and Type	Method	Description
double	ClusteringAdjustedRandIndexSimilarity.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1Similarity.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarity.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringRandIndexSimilarity.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarity.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1Similarity.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarity.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringRandIndexSimilarity.similarity(Clustering<?> o1, Clustering<?> o2)

Uses of Clustering in elki.visualization

Methods in elki.visualization that return Clustering

Modifier and Type	Method	Description
private Clustering<Model>	VisualizerContext.generateDefaultClustering()	Generate a default (fallback) clustering.

Uses of Clustering in elki.visualization.opticsplot

Methods in elki.visualization.opticsplot that return Clustering

Modifier and Type	Method	Description
static <E extends ClusterOrder> Clustering<Model>	OPTICSCut.makeOPTICSCut(E co, double epsilon)	Compute an OPTICS cut clustering

Uses of Clustering in elki.visualization.projections

Methods in elki.visualization.projections that return Clustering

Modifier and Type	Method	Description
Clustering<?>	SilhouetteProjection.getResult()	Get the underlying Clustering from which the silhouette values are calculated.

Uses of Clustering in elki.visualization.projector

Fields in elki.visualization.projector declared as Clustering

Modifier and Type	Field	Description
private Clustering<?>	SilhouettePlotProjector.clustering	Underlying clustering

Methods in elki.visualization.projector that return Clustering

Modifier and Type	Method	Description
Clustering<?>	SilhouettePlotProjector.getResult()	Get the underlying Clustering

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

Modifier and Type	Method	Description
private static DoubleDBIDList[]	SilhouettePlotProjector.sortSilhouette(Clustering<?> c, DoubleRelation dds)	Sort the silhouettes for visualization.

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

Constructor	Description
SilhouettePlotProjector(Clustering<?> clustering, DoubleRelation dds)	Constructor.

Uses of Clustering in elki.visualization.style

Fields in elki.visualization.style declared as Clustering

Modifier and Type	Field	Description
(package private) Clustering<?>	ClusterStylingPolicy.clustering	Clustering in use.

Methods in elki.visualization.style that return Clustering

Modifier and Type	Method	Description
Clustering<?>	ClusterStylingPolicy.getClustering()	Get the clustering used by this styling policy

Constructors in elki.visualization.style with parameters of type Clustering

Constructor	Description
ClusterStylingPolicy(Clustering<?> clustering, StyleLibrary style)	Constructor.

Uses of Clustering in elki.visualization.visualizers.actions

Fields in elki.visualization.visualizers.actions declared as Clustering

Modifier and Type	Field	Description
private Clustering<?>	ClusterStyleAction.SetStyleAction.c	Clustering to use

Constructors in elki.visualization.visualizers.actions with parameters of type Clustering

Constructor	Description
SetStyleAction(Clustering<?> c, VisualizerContext context)	Constructor.

Uses of Clustering in elki.visualization.visualizers.optics

Fields in elki.visualization.visualizers.optics declared as Clustering

Modifier and Type	Field	Description
(package private) Clustering<OPTICSModel>	OPTICSClusterVisualization.Instance.clus	Our clustering

Methods in elki.visualization.visualizers.optics with parameters of type Clustering

Modifier and Type	Method	Description
private void	OPTICSClusterVisualization.Instance.drawClusters(Clustering<OPTICSModel> clustering, It<Cluster<OPTICSModel>> clusters, int depth, java.util.Map<Cluster<?>,java.lang.String> colormap)	Recursively draw clusters

Uses of Clustering in elki.visualization.visualizers.visunproj

Methods in elki.visualization.visualizers.visunproj with parameters of type Clustering

Modifier and Type	Method	Description
protected static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c)	Compute the size of the clustering.

Uses of Clustering in tutorial.clustering

Methods in tutorial.clustering that return Clustering

Modifier and Type	Method	Description
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
Clustering<MeanModel>	SameSizeKMeans.run(Relation<V> relation)	Run k-means with cluster size constraints.