Uses of Interface
elki.data.model.Model

Packages that use Model

Package	Description
elki.clustering	Clustering 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.models
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
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.data	Basic classes for different data types, database object types and label types.
elki.data.model	Cluster models classes for various algorithms.
elki.data.synthetic.bymodel	Generator using a distribution model specified in an XML configuration file.
elki.datasource.parser	Parsers for different file formats and data types.
elki.evaluation.clustering	Evaluation of clustering results.
elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
elki.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
elki.result	Result types, representation and handling.
elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...).
elki.visualization	Visualization package of ELKI.
elki.visualization.opticsplot	Code for drawing OPTICS plots.
elki.visualization.visualizers.scatterplot.cluster	Visualizers for clustering results based on 2D projections.
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 Model in elki.clustering

Classes in elki.clustering with type parameters of type Model

Modifier and Type	Interface	Description
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 types with arguments of type Model

Modifier and Type	Method	Description
Clustering<Model>	SNNClustering.run(Relation<O> relation)	Perform SNN clustering

Uses of Model in elki.clustering.correlation

Methods in elki.clustering.correlation that return types with arguments of type Model

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<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.

Method parameters in elki.clustering.correlation with type arguments of type Model

Modifier and Type	Method	Description
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 Model in elki.clustering.dbscan

Methods in elki.clustering.dbscan that return types with arguments of type Model

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 Model in elki.clustering.dbscan.parallel

Methods in elki.clustering.dbscan.parallel that return types with arguments of type Model

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

Uses of Model in elki.clustering.em.models

Classes in elki.clustering.em.models with type parameters of type Model

Modifier and Type	Interface	Description
interface	EMClusterModel<O,M extends Model>	Models usable in EM clustering.
interface	EMClusterModelFactory<O,M extends Model>	Factory for initializing the EM models.

Uses of Model in elki.clustering.hierarchical.extraction

Methods in elki.clustering.hierarchical.extraction that return types with arguments of type Model

Modifier and Type	Method	Description
Clustering<Model>	ClustersWithNoiseExtraction.autorun(Database database)
Clustering<Model>	ClustersWithNoiseExtraction.Instance.run()	Extract all clusters from the pi-lambda-representation.
Clustering<Model>	ClustersWithNoiseExtraction.run(ClusterMergeHistory merges)	Process an existing result.

Uses of Model in elki.clustering.kmeans

Classes in elki.clustering.kmeans with type parameters of type Model

Modifier and Type	Class	Description
class	AbstractKMeans<V extends NumberVector,M extends Model>	Abstract base class for k-means implementations.
interface	KMeans<V extends NumberVector,M extends Model>	Some constants and options shared among kmeans family algorithms.

Uses of Model in elki.clustering.meta

Methods in elki.clustering.meta that return types with arguments of type Model

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

Uses of Model in elki.clustering.subspace

Methods in elki.clustering.subspace that return types with arguments of type Model

Modifier and Type	Method	Description
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.

Method parameters in elki.clustering.subspace with type arguments of type Model

Modifier and Type	Method	Description
private Subspace	SUBCLU.bestSubspace(java.util.List<Subspace> subspaces, Subspace candidate, java.util.TreeMap<Subspace,java.util.List<Cluster<Model>>> clusterMap)	Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.

Uses of Model in elki.clustering.svm

Methods in elki.clustering.svm that return types with arguments of type Model

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

Uses of Model in elki.clustering.trivial

Classes in elki.clustering.trivial with type parameters of type Model

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

Methods in elki.clustering.trivial that return types with arguments of type Model

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.

Method parameters in elki.clustering.trivial with type arguments of type Model

Modifier and Type	Method	Description
Clustering<Model>	ByModelClustering.run(Relation<Model> relation)	Run the actual clustering algorithm.

Uses of Model in elki.data

Classes in elki.data with type parameters of type Model

Modifier and Type	Class	Description
class	Cluster<M extends Model>	Generic cluster class, that may or not have hierarchical information.
class	Clustering<M extends Model>	Result class for clusterings.

Fields in elki.data declared as Model

Modifier and Type	Field	Description
private M	Cluster.model	Cluster model.

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

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 Model in elki.data.model

Subinterfaces of Model in elki.data.model

Modifier and Type	Interface	Description
interface	PrototypeModel<V>	Cluster model that stores a prototype for each cluster.

Classes in elki.data.model that implement Model

Modifier and Type	Class	Description
class	BiclusterModel	Wrapper class to provide the basic properties of a Bicluster.
class	BiclusterWithInversionsModel	This code was factored out of the Bicluster class, since not all biclusters have inverted rows.
class	ClusterModel	Generic cluster model.
class	CoreObjectsModel	Cluster model using "core" objects.
class	CorrelationAnalysisSolution	A solution of correlation analysis is a matrix of equations describing the dependencies.
class	CorrelationModel	Cluster model using a filtered PCA result and an centroid.
class	DendrogramModel	Model for dendrograms, provides the height of this subtree.
class	DimensionModel	Cluster model additionally providing a cluster dimensionality.
class	EMModel	Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	GeneratorModel	Cluster model for synthetically generated data.
class	KMeansModel	Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).
class	LinearEquationModel	Cluster model containing a linear equation system for the cluster.
class	MeanModel	Cluster model that stores a mean for the cluster.
class	MedoidModel	Cluster model that stores a mean for the cluster.
class	OPTICSModel	Model for an OPTICS cluster
class	PrototypeDendrogramModel	Hierarchical cluster, with prototype.
class	SimplePrototypeModel<V>	Cluster model that stores a prototype for each cluster.
class	SubspaceModel	Model for Subspace Clusters.

Fields in elki.data.model with type parameters of type Model

Modifier and Type	Field	Description
static SimpleTypeInformation<Model>	Model.TYPE	Type information, for relation selection.

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

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.

Uses of Model in elki.data.synthetic.bymodel

Methods in elki.data.synthetic.bymodel that return Model

Modifier and Type	Method	Description
Model	GeneratorInterface.makeModel()	Make a cluster model for this cluster.
Model	GeneratorSingleCluster.makeModel()
Model	GeneratorStatic.makeModel()

Methods in elki.data.synthetic.bymodel with parameters of type Model

Modifier and Type	Method	Description
private void	GeneratorMain.initLabelsAndModels(java.util.ArrayList<GeneratorInterface> generators, ClassLabel[] labels, Model[] models, java.util.regex.Pattern reassign)	Initialize cluster labels and models.

Uses of Model in elki.datasource.parser

Fields in elki.datasource.parser with type parameters of type Model

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

Uses of Model in elki.evaluation.clustering

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

Modifier and Type	Method	Description
static <C extends Model> void	LogClusterSizes.logClusterSizes(Clustering<C> c)	Log the cluster sizes of a clustering.

Uses of Model in elki.evaluation.outlier

Methods in elki.evaluation.outlier that return types with arguments of type Model

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

Uses of Model in elki.outlier.trivial

Method parameters in elki.outlier.trivial with type arguments of type Model

Modifier and Type	Method	Description
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels)	Run the algorithm

Uses of Model in elki.result

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

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.
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.
private void	KMLOutputHandler.writeClusteringResult(javax.xml.stream.XMLStreamWriter xmlw, Clustering<Model> clustering, Database database)

Uses of Model in elki.result.textwriter

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

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

Uses of Model in elki.visualization

Methods in elki.visualization that return types with arguments of type Model

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

Uses of Model in elki.visualization.opticsplot

Methods in elki.visualization.opticsplot that return types with arguments of type Model

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

Uses of Model in elki.visualization.visualizers.scatterplot.cluster

Method parameters in elki.visualization.visualizers.scatterplot.cluster with type arguments of type Model

Modifier and Type	Method	Description
private double	ClusterAlphaHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus)	Recursively add a cluster and its children for alpha shapes.
private double	ClusterAlphaHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus)	Recursively add a cluster and its children for alpha shapes.
private DoubleObjPair<Polygon>	ClusterConvexHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls)	Recursively step through the clusters to build the hulls.
private DoubleObjPair<Polygon>	ClusterConvexHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls)	Recursively step through the clusters to build the hulls.

Uses of Model in elki.visualization.visualizers.visunproj

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

Modifier and Type	Method	Description
protected static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c)	Compute the size of the clustering.
private static <M extends Model> void	KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier, Cluster<M> cluster, int[] size)	Recursive depth computation.

Method parameters in elki.visualization.visualizers.visunproj with type arguments of type Model

Modifier and Type	Method	Description
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)

Uses of Model in tutorial.clustering

Methods in tutorial.clustering that return types with arguments of type Model

Modifier and Type	Method	Description
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