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.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings.
elki.data
elki.datasource.parser
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.result
elki.similarity.cluster	Similarity measures for comparing clusters.

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(elki.database.relation.Relation<elki.data.NumberVector> relation)	Run the clustering algorithm.
Clustering<PrototypeModel<O>>	CanopyPreClustering.run(elki.database.relation.Relation<O> relation)	Run the canopy clustering algorithm
Clustering<SimplePrototypeModel<elki.database.ids.DBID>>	CFSFDP.run(elki.database.relation.Relation<O> relation)	Perform CFSFDP clustering.
Clustering<PrototypeModel<O>>	Leader.run(elki.database.relation.Relation<O> relation)	Run the leader clustering algorithm.
Clustering<MeanModel>	NaiveMeanShiftClustering.run(elki.database.relation.Relation<V> relation)	Run the mean-shift clustering algorithm.
Clustering<Model>	SNNClustering.run(elki.database.relation.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(elki.database.ids.ArrayDBIDs ids, int[] assignment)	Build the clustering result.
Clustering<MedoidModel>	AffinityPropagation.run(elki.database.relation.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(elki.database.relation.Relation<? extends elki.data.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
Clustering<DimensionModel>	COPAC.run(elki.database.Database database, elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Run the COPAC algorithm.
Clustering<CorrelationModel>	ERiC.run(elki.database.Database database, elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Performs the ERiC algorithm on the given database.
Clustering<Model>	LMCLUS.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	ORCLUS.run(elki.database.relation.Relation<? extends elki.data.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, 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.

Uses of Clustering in elki.clustering.dbscan

Methods in elki.clustering.dbscan that return Clustering

Modifier and Type	Method	Description
Clustering<Model>	GeneralizedDBSCAN.autorun(elki.database.Database database)
protected Clustering<Model>	GriDBSCAN.Instance.buildResult(elki.database.ids.DBIDs ids, int clusterid)	Assemble the clustering result.
Clustering<Model>	DBSCAN.run(elki.database.relation.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(elki.database.relation.Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(elki.database.relation.Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	LSDBC.run(elki.database.relation.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(elki.database.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(elki.database.relation.Relation<elki.data.NumberVector> relation)	Run the clustering algorithm.
Clustering<M>	EM.run(elki.database.relation.Relation<O> relation)	Performs the EM clustering algorithm on the given database.
Clustering<EMModel>	KDTreeEM.run(elki.database.relation.Relation<? extends elki.data.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(elki.database.relation.Relation<elki.data.NumberVector> relation)	Run the clustering algorithm.
Clustering<KMeansModel>	BIRCHLloydKMeans.run(elki.database.relation.Relation<elki.data.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(elki.database.Database database)
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.autorun(elki.database.Database database)
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.autorun(elki.database.Database database)
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildFlat(int split, elki.logging.progress.FiniteProgress progress)	Build a flat clustering.
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildHierarchical(int split, elki.logging.progress.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(elki.database.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, 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
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(elki.database.relation.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, elki.database.relation.Relation<? extends elki.data.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(elki.database.relation.Relation<V> relation)
Clustering<M>	BestOfMultipleKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	BetulaLloydKMeans.run(elki.database.relation.Relation<elki.data.NumberVector> relation)	Run the clustering algorithm.
Clustering<M>	BisectingKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	CompareMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	ElkanKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	ExponionKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<MeanModel>	FuzzyCMeans.run(elki.database.relation.Relation<V> relation)	Runs Fuzzy C Means clustering on the given Relation
Clustering<KMeansModel>	HamerlyKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	HartiganWongKMeans.run(elki.database.relation.Relation<V> rel)
Clustering<KMeansModel>	KDTreeFilteringKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	KDTreePruningKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<M>	KMeans.run(elki.database.relation.Relation<V> rel)	Run the clustering algorithm.
Clustering<KMeansModel>	KMeansMinusMinus.run(elki.database.relation.Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	LloydKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	MacQueenKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	ShallotKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SimplifiedElkanKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SingleAssignmentKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SortMeans.run(elki.database.relation.Relation<V> relation)
Clustering<M>	XMeans.run(elki.database.relation.Relation<V> relation)	Run the algorithm on a database and relation.
Clustering<KMeansModel>	YinYangKMeans.run(elki.database.relation.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(elki.database.relation.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(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionXMeans.logLikelihoodXMeans(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionZhao.logLikelihoodZhao(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(elki.database.relation.Relation<? extends elki.data.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 elki.data.NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	AkaikeInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)	Calculates and returns the quality measure.
<V extends elki.data.NumberVector> double	WithinClusterMeanDistance.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	WithinClusterVariance.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.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(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	EuclideanSphericalHamerlyKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	EuclideanSphericalSimplifiedElkanKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalElkanKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalHamerlyKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalSimplifiedElkanKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalSimplifiedHamerlyKMeans.run(elki.database.relation.Relation<V> relation)
Clustering<KMeansModel>	SphericalSingleAssignmentKMeans.run(elki.database.relation.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(elki.database.relation.Relation<O> relation)
Clustering<MedoidModel>	AlternatingKMedoids.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	CLARA.run(elki.database.relation.Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(elki.database.relation.Relation<V> relation, int k, elki.database.query.distance.DistanceQuery<? super V> distQ)
Clustering<MedoidModel>	CLARANS.run(elki.database.relation.Relation<O> relation)	Run CLARANS clustering.
Clustering<MedoidModel>	CLARANS.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	EagerPAM.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastCLARA.run(elki.database.relation.Relation<V> relation)
Clustering<MedoidModel>	FastCLARA.run(elki.database.relation.Relation<V> relation, int k, elki.database.query.distance.DistanceQuery<? super V> distQ)
Clustering<MedoidModel>	FastCLARANS.run(elki.database.relation.Relation<V> relation)
Clustering<MedoidModel>	FasterCLARA.run(elki.database.relation.Relation<O> relation)
Clustering<MedoidModel>	FasterCLARA.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FasterPAM.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastPAM.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastPAM1.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	KMedoidsClustering.run(elki.database.relation.Relation<O> relation)	Run k-medoids clustering.
Clustering<MedoidModel>	KMedoidsClustering.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.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(elki.database.relation.Relation<O> relation)
Clustering<MedoidModel>	PAM.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	ReynoldsPAM.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	SingleAssignmentKMedoids.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
protected static Clustering<MedoidModel>	PAM.wrapResult(elki.database.ids.DBIDs ids, elki.database.datastore.WritableIntegerDataStore assignment, elki.database.ids.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(elki.database.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(elki.database.relation.Relation<? extends elki.data.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(elki.database.Database database)
private Clustering<OPTICSModel>	OPTICSXi.ClusterHierarchyBuilder.build(ClusterOrder clusterOrder, elki.database.ids.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(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	FastMSC.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	PAMMEDSIL.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)
Clustering<MedoidModel>	PAMSIL.run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)

Uses of Clustering in elki.clustering.subspace

Methods in elki.clustering.subspace that return Clustering

Modifier and Type	Method	Description
Clustering<SubspaceModel>	CLIQUE.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Performs the CLIQUE algorithm on the given database.
Clustering<SubspaceModel>	DOC.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Performs the DOC or FastDOC (as configured) algorithm.
Clustering<SubspaceModel>	P3C.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Performs the P3C algorithm on the given Database.
<V extends elki.data.NumberVector> Clustering<SubspaceModel>	PROCLUS.run(elki.database.relation.Relation<V> relation)	Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel>	SUBCLU.run(elki.database.relation.Relation<V> relation)	Performs the SUBCLU algorithm on the given database.

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(elki.database.Database database)
Clustering<Model>	ByLabelHierarchicalClustering.autorun(elki.database.Database database)
Clustering<Model>	ByLabelOrAllInOneClustering.autorun(elki.database.Database database)
Clustering<Model>	ByLabelClustering.run(elki.database.relation.Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	ByLabelHierarchicalClustering.run(elki.database.relation.Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(elki.database.relation.Relation<?> relation)	Perform trivial clustering.
Clustering<Model>	TrivialAllNoise.run(elki.database.relation.Relation<?> relation)	Run the trivial clustering algorithm.

Uses of Clustering in elki.data

Fields in elki.data with type parameters of type Clustering

Modifier and Type	Field	Description
static elki.data.type.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(elki.database.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(elki.database.relation.Relation<? extends O> rel, elki.database.query.distance.DistanceQuery<O> dq, Clustering<?> c)	Evaluate a single clustering.
double	ClusterRadius.evaluateClustering(elki.database.Database db, elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	ConcordantPairsGammaTau.evaluateClustering(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	DaviesBouldinIndex.evaluateClustering(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	DBCV.evaluateClustering(elki.database.relation.Relation<O> relation, Clustering<?> cl)	Evaluate a single clustering.
double	PBMIndex.evaluateClustering(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	Silhouette.evaluateClustering(elki.database.relation.Relation<O> rel, elki.database.query.distance.DistanceQuery<O> dq, Clustering<?> c)	Evaluate a single clustering.
double	SimplifiedSilhouette.evaluateClustering(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	SquaredErrors.evaluateClustering(elki.database.relation.Relation<? extends elki.data.NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	VarianceRatioCriterion.evaluateClustering(elki.database.relation.Relation<? extends elki.data.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.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.

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<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusteringAdjustedRandIndexSimilarity.instantiate(elki.database.relation.Relation<T> relation)
<T extends Clustering<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusteringBCubedF1Similarity.instantiate(elki.database.relation.Relation<T> relation)
<T extends Clustering<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusteringDistanceSimilarity.instantiate(elki.database.relation.Relation<T> relation)
<T extends Clustering<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusteringFowlkesMallowsSimilarity.instantiate(elki.database.relation.Relation<T> relation)
<T extends Clustering<?>> elki.database.query.DistanceSimilarityQuery<T>	ClusteringRandIndexSimilarity.instantiate(elki.database.relation.Relation<T> relation)

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

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