Uses of Class
de.lmu.ifi.dbs.elki.data.Cluster

Packages that use Cluster

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
de.lmu.ifi.dbs.elki.evaluation.scores	Evaluation of rankings and scorings.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...)
de.lmu.ifi.dbs.elki.result.textwriter.naming	Naming schemes for clusters (for output when an algorithm doesn't generate cluster names).
de.lmu.ifi.dbs.elki.visualization.style	Style management for ELKI visualizations.
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster	Visualizers for clustering results based on 2D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj	Visualizers that do not use a particular projection.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that return Cluster

Modifier and Type	Method and Description
protected Cluster<BiclusterModel>	AbstractBiclustering.defineBicluster(BitSet rows, BitSet cols) Defines a Bicluster as given by the included rows and columns.
protected Cluster<BiclusterModel>	AbstractBiclustering.defineBicluster(long[] rows, long[] cols) Defines a Bicluster as given by the included rows and columns.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return types with arguments of type Cluster

Modifier and Type	Method and Description
private List<List<Cluster<CorrelationModel<V>>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<V> database, 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.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Cluster

Modifier and Type	Method and Description
private boolean	ERiC.isParent(ERiCNeighborPredicate.Instance npred, Cluster<CorrelationModel<V>> parent, Hierarchy.Iter<Cluster<CorrelationModel<V>>> iter) Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type arguments of type Cluster

Modifier and Type	Method and Description
private void	ERiC.buildHierarchy(Clustering<CorrelationModel<V>> clustering, List<List<Cluster<CorrelationModel<V>>>> clusterMap, ERiCNeighborPredicate.Instance npred)
private boolean	ERiC.isParent(ERiCNeighborPredicate.Instance npred, Cluster<CorrelationModel<V>> parent, Hierarchy.Iter<Cluster<CorrelationModel<V>>> iter) Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction with type parameters of type Cluster

Modifier and Type	Field and Description
protected Collection<Cluster<DendrogramModel>>	SimplifiedHierarchyExtraction.TempCluster.children (Finished) child clusters

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction that return Cluster

Modifier and Type	Method and Description
private Cluster<DendrogramModel>	ExtractFlatClusteringFromHierarchy.makeCluster(DBIDRef lead, double depth, DBIDs members) Make the cluster for the given object
private Cluster<DendrogramModel>	SimplifiedHierarchyExtraction.makeSingletonCluster(DBIDRef lead, double depth) Make the cluster for the given object
private Cluster<DendrogramModel>	SimplifiedHierarchyExtraction.TempCluster.toCluster(Clustering<DendrogramModel> clustering, DBIDRef lead) Make the cluster for the given object

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction with parameters of type Cluster

Modifier and Type	Method and Description
void	SimplifiedHierarchyExtraction.TempCluster.addChild(Cluster<DendrogramModel> clu) Add a child cluster.
private void	HDBSCANHierarchyExtraction.TempCluster.collectChildren(Clustering<DendrogramModel> clustering, HDBSCANHierarchyExtraction.TempCluster cur, Cluster<DendrogramModel> clus, boolean flatten, boolean hierarchical) Recursive flattening of clusters.
private void	HDBSCANHierarchyExtraction.TempCluster.finalizeCluster(Clustering<DendrogramModel> clustering, Cluster<DendrogramModel> parent, boolean flatten, boolean hierarchical) Make the cluster for the given object

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return types with arguments of type Cluster

Modifier and Type	Method and Description
protected List<Cluster<M>>	XMeans.splitCluster(Cluster<M> parentCluster, Database database, Relation<V> relation) Conditionally splits the clusters based on the information criterion.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type Cluster

Modifier and Type	Method and Description
protected List<? extends NumberVector>	XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation) Split an existing centroid into two initial centers.
protected List<Cluster<M>>	XMeans.splitCluster(Cluster<M> parentCluster, Database database, Relation<V> relation) Conditionally splits the clusters based on the information criterion.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with type arguments of type Cluster

Modifier and Type	Method and Description
void	PredefinedInitialMeans.setInitialClusters(List<? extends Cluster<? extends MeanModel>> initialMeans) Set the initial means.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with parameters of type Cluster

Modifier and Type	Method and Description
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Variance contribution of a single cluster.

Uses of Cluster in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return Cluster

Modifier and Type	Method and Description
private Cluster<SubspaceModel>	DOC.makeCluster(Relation<V> relation, DBIDs C, long[] D) Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private Cluster<SubspaceModel>	DOC.runDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of DOC, finding a single cluster.
private Cluster<SubspaceModel>	DOC.runFastDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r) Performs a single run of FastDOC, finding a single cluster.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return types with arguments of type Cluster

Modifier and Type	Method and Description
private 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.
private List<Cluster<SubspaceModel>>	DiSH.sortClusters(Relation<V> relation, TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap) Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Cluster

Modifier and Type	Method and Description
private boolean	DiSH.isParent(Relation<V> relation, Cluster<SubspaceModel> parent, Hierarchy.Iter<Cluster<SubspaceModel>> iter, int db_dim) Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type Cluster

Modifier and Type	Method and Description
private Subspace	SUBCLU.bestSubspace(List<Subspace> subspaces, Subspace candidate, TreeMap<Subspace,List<Cluster<Model>>> clusterMap) Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.
private void	DiSH.buildHierarchy(Relation<V> database, Clustering<SubspaceModel> clustering, List<Cluster<SubspaceModel>> clusters, int dimensionality) Builds the cluster hierarchy.
private boolean	DiSH.isParent(Relation<V> relation, Cluster<SubspaceModel> parent, Hierarchy.Iter<Cluster<SubspaceModel>> iter, int db_dim) Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Uses of Cluster in de.lmu.ifi.dbs.elki.data

Fields in de.lmu.ifi.dbs.elki.data with type parameters of type Cluster

Modifier and Type	Field and Description
static Comparator<Cluster<?>>	Cluster.BY_NAME_SORTER A partial comparator for Clusters, based on their name.
private ModifiableHierarchy<Cluster<M>>	Clustering.hierarchy Cluster hierarchy.
private List<Cluster<M>>	Clustering.toplevelclusters Keep a list of top level clusters.

Methods in de.lmu.ifi.dbs.elki.data that return types with arguments of type Cluster

Modifier and Type	Method and Description
List<Cluster<M>>	Clustering.getAllClusters() Collect all clusters (recursively) into a List.
Hierarchy<Cluster<M>>	Clustering.getClusterHierarchy() Get the cluster hierarchy.
List<Cluster<M>>	Clustering.getToplevelClusters() Return top level clusters
Hierarchy.Iter<Cluster<M>>	Clustering.iterToplevelClusters() Iterate over the top level clusters.

Methods in de.lmu.ifi.dbs.elki.data with parameters of type Cluster

Modifier and Type	Method and Description
void	Clustering.addChildCluster(Cluster<M> parent, Cluster<M> child) Add a cluster to the clustering.
void	Clustering.addChildCluster(Cluster<M> parent, Cluster<M> child) Add a cluster to the clustering.
void	Clustering.addToplevelCluster(Cluster<M> clus) Add a cluster to the clustering.

Constructor parameters in de.lmu.ifi.dbs.elki.data with type arguments of type Cluster

Constructor and Description
Clustering(String name, String shortname, List<Cluster<M>> toplevelclusters) Constructor with a list of top level clusters

Uses of Cluster in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster with type parameters of type Cluster

Modifier and Type	Method and Description
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterJaccardSimilarityFunction.instantiate(Relation<T> relation)
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterIntersectionSimilarityFunction.instantiate(Relation<T> relation)

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster that return types with arguments of type Cluster

Modifier and Type	Method and Description
SimpleTypeInformation<? super Cluster<?>>	ClusterJaccardSimilarityFunction.getInputTypeRestriction()
SimpleTypeInformation<? super Cluster<?>>	ClusterIntersectionSimilarityFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster with parameters of type Cluster

Modifier and Type	Method and Description
double	ClusterJaccardSimilarityFunction.distance(Cluster<?> o1, Cluster<?> o2)
double	ClusterJaccardSimilarityFunction.distance(Cluster<?> o1, Cluster<?> o2)
double	ClusterIntersectionSimilarityFunction.distance(Cluster<?> o1, Cluster<?> o2)
double	ClusterIntersectionSimilarityFunction.distance(Cluster<?> o1, Cluster<?> o2)
double	ClusterJaccardSimilarityFunction.similarity(Cluster<?> o1, Cluster<?> o2)
double	ClusterJaccardSimilarityFunction.similarity(Cluster<?> o1, Cluster<?> o2)
double	ClusterIntersectionSimilarityFunction.similarity(Cluster<?> o1, Cluster<?> o2)
double	ClusterIntersectionSimilarityFunction.similarity(Cluster<?> o1, Cluster<?> o2)

Uses of Cluster in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Method parameters in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with type arguments of type Cluster

Modifier and Type	Method and Description
static int	EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Compute centroids.
protected double[]	EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel, List<? extends Cluster<?>> clusters, int withinPairs)
static int	EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Update the global centroid.
double[]	EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel, List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Uses of Cluster in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments with type parameters of type Cluster

Modifier and Type	Field and Description
private List<List<? extends Cluster<?>>>	Segments.clusters Clusters

Method parameters in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments with type arguments of type Cluster

Modifier and Type	Method and Description
private void	Segments.recursivelyFill(List<List<? extends Cluster<?>>> cs)
private void	Segments.recursivelyFill(List<List<? extends Cluster<?>>> cs, int depth, SetDBIDs first, SetDBIDs second, int[] path, boolean objectsegment)

Uses of Cluster in de.lmu.ifi.dbs.elki.evaluation.scores

Methods in de.lmu.ifi.dbs.elki.evaluation.scores with parameters of type Cluster

Modifier and Type	Method and Description
double	ScoreEvaluation.evaluate(Cluster<?> clus, DoubleDBIDList nei) Evaluate given a cluster (of positive elements) and a scoring list.
double	AbstractScoreEvaluation.evaluate(Cluster<?> clus, DoubleDBIDList nei)

Uses of Cluster in de.lmu.ifi.dbs.elki.result

Methods in de.lmu.ifi.dbs.elki.result with parameters of type Cluster

Modifier and Type	Method and Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, Map<Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords) Recursively step through the clusters to build the hulls.
private StringBuilder	KMLOutputHandler.makeDescription(Cluster<?> c) Make an HTML description.

Method parameters in de.lmu.ifi.dbs.elki.result with type arguments of type Cluster

Modifier and Type	Method and Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, Map<Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords) Recursively step through the clusters to build the hulls.

Uses of Cluster in de.lmu.ifi.dbs.elki.result.textwriter

Methods in de.lmu.ifi.dbs.elki.result.textwriter with parameters of type Cluster

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

Uses of Cluster in de.lmu.ifi.dbs.elki.result.textwriter.naming

Fields in de.lmu.ifi.dbs.elki.result.textwriter.naming with type parameters of type Cluster

Modifier and Type	Field and Description
private Map<Cluster<?>,String>	SimpleEnumeratingScheme.names Assigned cluster names.

Methods in de.lmu.ifi.dbs.elki.result.textwriter.naming with parameters of type Cluster

Modifier and Type	Method and Description
String	SimpleEnumeratingScheme.getNameFor(Cluster<?> cluster) Retrieve the cluster name.
String	NamingScheme.getNameFor(Cluster<?> cluster) Retrieve a name for the given cluster.

Uses of Cluster in de.lmu.ifi.dbs.elki.visualization.style

Fields in de.lmu.ifi.dbs.elki.visualization.style with type parameters of type Cluster

Modifier and Type	Field and Description
(package private) TObjectIntMap<Cluster<?>>	ClusterStylingPolicy.cmap Map from cluster objects to color offsets.

Methods in de.lmu.ifi.dbs.elki.visualization.style with parameters of type Cluster

Modifier and Type	Method and Description
int	ClusterStylingPolicy.getStyleForCluster(Cluster<?> c) Get the style number for a cluster.

Uses of Cluster in de.lmu.ifi.dbs.elki.visualization.visualizers.optics

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.optics with type arguments of type Cluster

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

Uses of Cluster in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster with parameters of type Cluster

Modifier and Type	Method and Description
private double	ClusterHullVisualization.Instance.addRecursively(ArrayList<Vector> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus) Recursively add a cluster and its children.
private DoubleObjPair<Polygon>	ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, Map<Object,DoubleObjPair<Polygon>> hulls) Recursively step through the clusters to build the hulls.

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster with type arguments of type Cluster

Modifier and Type	Method and Description
private double	ClusterHullVisualization.Instance.addRecursively(ArrayList<Vector> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus) Recursively add a cluster and its children.
private DoubleObjPair<Polygon>	ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, Map<Object,DoubleObjPair<Polygon>> hulls) Recursively step through the clusters to build the hulls.

Uses of Cluster in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with parameters of type Cluster

Modifier and Type	Method and Description
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, TObjectIntMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private static <M extends Model> void	KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier, Cluster<M> cluster, int[] size) Recursive depth computation.

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with type arguments of type Cluster

Modifier and Type	Method and Description
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, TObjectIntMap<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, TObjectIntMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private static <M extends Model> void	KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier, Cluster<M> cluster, int[] size) Recursive depth computation.