Uses of Class
elki.data.Cluster

Packages that use Cluster

Package	Description
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.clustering.optics	OPTICS family of clustering algorithms.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.data
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.similarity.cluster	Similarity measures for comparing clusters.

Uses of Cluster in elki.clustering.biclustering

Methods in elki.clustering.biclustering that return Cluster

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

Uses of Cluster in elki.clustering.correlation

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

Modifier and Type	Method	Description
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, elki.database.relation.Relation<? extends elki.data.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.

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

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

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

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 boolean	ERiC.isParent(ERiCNeighborPredicate.Instance npred, Cluster<CorrelationModel> parent, elki.utilities.datastructures.iterator.It<Cluster<CorrelationModel>> iter)	Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Uses of Cluster in elki.clustering.hierarchical.extraction

Fields in elki.clustering.hierarchical.extraction with type parameters of type Cluster

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

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

Modifier and Type	Method	Description
protected Cluster<DendrogramModel>	AbstractCutDendrogram.Instance.makeCluster(int seq, elki.database.ids.DBIDs members)	Make the cluster for the given object
protected Cluster<DendrogramModel>	SimplifiedHierarchyExtraction.Instance.makeCluster(int seq, double depth, elki.database.ids.DBIDs members)	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

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

Modifier and Type	Method	Description
void	SimplifiedHierarchyExtraction.TempCluster.addChild(Cluster<DendrogramModel> clu)	Add a child cluster.
private double	HDBSCANHierarchyExtraction.Instance.collectChildren(HDBSCANHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, elki.database.datastore.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, elki.database.datastore.WritableDoubleDataStore glosh, Cluster<DendrogramModel> parent, boolean flatten)	Make the cluster for the given object

Uses of Cluster in elki.clustering.kmeans

Methods in elki.clustering.kmeans that return types with arguments of type Cluster

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

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

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

Uses of Cluster in elki.clustering.kmeans.initialization

Method parameters in elki.clustering.kmeans.initialization with type arguments of type Cluster

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

Uses of Cluster in elki.clustering.kmeans.quality

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

Modifier and Type	Method	Description
static double	AbstractKMeansQualityMeasure.varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, elki.distance.NumberVectorDistance<?> distance, elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Variance contribution of a single cluster.

Uses of Cluster in elki.clustering.optics

Fields in elki.clustering.optics with type parameters of type Cluster

Modifier and Type	Field	Description
(package private) java.util.HashSet<Cluster<OPTICSModel>>	OPTICSXi.ClusterHierarchyBuilder.curclusters	Current "unattached" clusters.

Uses of Cluster in elki.clustering.subspace

Methods in elki.clustering.subspace that return Cluster

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

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

Modifier and Type	Method	Description
private java.util.List<Cluster<Model>>	SUBCLU.runDBSCAN(elki.database.relation.Relation<V> relation, elki.database.ids.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 Cluster

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 Cluster in elki.data

Fields in elki.data with type parameters of type Cluster

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

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

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

Methods in elki.data with parameters of type Cluster

Modifier and Type	Method	Description
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 elki.data with type arguments of type Cluster

Constructor	Description
Clustering(java.util.List<Cluster<M>> toplevelclusters)	Constructor with a list of top level clusters

Uses of Cluster in elki.evaluation.clustering

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

Modifier and Type	Method	Description
static double	EvaluateClustering.evaluateRanking(elki.evaluation.scores.ScoreEvaluation eval, Cluster<?> clus, elki.database.ids.DoubleDBIDList ranking)	Evaluate given a cluster (of positive elements) and a scoring list.

Uses of Cluster in elki.evaluation.clustering.internal

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

Modifier and Type	Method	Description
protected double	CIndex.processCluster(Cluster<?> cluster, java.util.List<? extends Cluster<?>> clusters, int i, elki.database.query.distance.DistanceQuery<O> dq, elki.utilities.datastructures.heap.DoubleHeap maxDists, elki.utilities.datastructures.heap.DoubleHeap minDists, int w)
protected void	CIndex.processSingleton(Cluster<?> cluster, elki.database.relation.Relation<? extends O> rel, elki.database.query.distance.DistanceQuery<O> dq, elki.utilities.datastructures.heap.DoubleHeap maxDists, elki.utilities.datastructures.heap.DoubleHeap minDists, int w)

Method parameters in elki.evaluation.clustering.internal with type arguments of type Cluster

Modifier and Type	Method	Description
static int	SimplifiedSilhouette.centroids(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, elki.data.NumberVector[] centroids, NoiseHandling noiseOption)	Compute centroids.
protected double[]	ConcordantPairsGammaTau.computeWithinDistances(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
static int	VarianceRatioCriterion.globalCentroid(elki.math.linearalgebra.Centroid overallCentroid, elki.database.relation.Relation<? extends elki.data.NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, elki.data.NumberVector[] centroids, NoiseHandling noiseOption)	Update the global centroid.
protected double	CIndex.processCluster(Cluster<?> cluster, java.util.List<? extends Cluster<?>> clusters, int i, elki.database.query.distance.DistanceQuery<O> dq, elki.utilities.datastructures.heap.DoubleHeap maxDists, elki.utilities.datastructures.heap.DoubleHeap minDists, int w)
double[]	DaviesBouldinIndex.withinGroupDistances(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, elki.data.NumberVector[] centroids)

Uses of Cluster in elki.evaluation.clustering.pairsegments

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

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

Method parameters in elki.evaluation.clustering.pairsegments with type arguments of type Cluster

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

Uses of Cluster in elki.similarity.cluster

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

Modifier and Type	Method	Description
<T extends Cluster<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusterIntersectionSimilarity.instantiate(elki.database.relation.Relation<T> relation)
<T extends Cluster<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusterJaccardSimilarity.instantiate(elki.database.relation.Relation<T> relation)

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

Modifier and Type	Method	Description
elki.data.type.SimpleTypeInformation<? super Cluster<?>>	ClusterIntersectionSimilarity.getInputTypeRestriction()
elki.data.type.SimpleTypeInformation<? super Cluster<?>>	ClusterJaccardSimilarity.getInputTypeRestriction()

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

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