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

Packages that use Clustering

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
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.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional	Clustering algorithms for one-dimensional data.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types.
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
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.outlier	Evaluate an outlier score using a misclassification based cost model.
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	Visualization package of ELKI.
de.lmu.ifi.dbs.elki.visualization.opticsplot	Code for drawing OPTICS plots
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.parallel.cluster	Visualizers for clustering results based on parallel coordinates.
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.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Clustering

Modifier and Type	Class and Description
class	AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector> 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 de.lmu.ifi.dbs.elki.algorithm.clustering that return Clustering

Modifier and Type	Method and Description
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<PrototypeModel<O>>	CanopyPreClustering.run(Database database, Relation<O> relation) Run the algorithm
Clustering<MeanModel>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation that return Clustering

Modifier and Type	Method and Description
Clustering<MedoidModel>	AffinityPropagationClusteringAlgorithm.run(Database db, Relation<O> relation) Perform affinity propagation clustering.

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

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

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

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Clustering

Modifier and Type	Method and 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>	LMCLUS.run(Database database, Relation<NumberVector> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	CASH.run(Database database, Relation<V> vrel) Run CASH on the relation.
Clustering<DimensionModel>	COPAC.run(Database database, Relation<V> relation) Run the COPAC algorithm.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<CorrelationModel<V>>	ERiC.run(Database database, Relation<V> relation) Performs the ERiC algorithm on the given database.

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

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

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.em that return Clustering

Modifier and Type	Method and Description
Clustering<M>	EM.run(Database database, Relation<V> relation) Performs the EM clustering algorithm on the given database.

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan that return Clustering

Modifier and Type	Method and Description
Clustering<Model>	GeneralizedDBSCAN.Instance.run() Run the actual GDBSCAN algorithm.
Clustering<Model>	GeneralizedDBSCAN.run(Database database)
Clustering<Model>	LSDBC.run(Database database, Relation<O> relation) Run the LSDBC algorithm

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

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

Modifier and Type	Method and Description
private Clustering<DendrogramModel>	ExtractFlatClusteringFromHierarchy.extractClusters(DBIDs ids, DBIDDataStore pi, DoubleDataStore lambda) Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	ExtractFlatClusteringFromHierarchy.run(Database database)

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return Clustering

Modifier and Type	Method and Description
Clustering<KMeansModel>	KMeansBatchedLloyd.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsPAM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<M>	KMeansBisecting.run(Database database, Relation<V> relation)
Clustering<M>	KMeans.run(Database database, Relation<V> rel) Run the clustering algorithm.
Clustering<KMeansModel>	SingleAssignmentKMeans.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMacQueen.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsEM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<M>	XMeans.run(Database database, Relation<V> relation) Run the algorithm on a database and relation.
Clustering<KMeansModel>	KMeansHybridLloydMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansLloyd.run(Database database, Relation<V> relation)
Clustering<M>	BestOfMultipleKMeans.run(Database database, Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(Database database, Relation<V> relation)

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel that return Clustering

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

Uses of Clustering 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 Clustering

Modifier and Type	Method and Description
static double	AbstractKMeansQualityMeasure.logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction) Computes log likelihood of an entire clustering.
static double	AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction) 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	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, PrimitiveDistanceFunction<? super NumberVector> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional that return Clustering

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

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.optics that return Clustering

Modifier and Type	Method and Description
private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrderResult<DoubleDistanceClusterOrderEntry> clusterOrderResult, Relation<?> relation, double ixi, int minpts) Extract clusters from a cluster order result.
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)

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

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as Clustering

Modifier and Type	Field and Description
private Clustering<SubspaceModel>	SUBCLU.result Holds the result;

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

Modifier and Type	Method and Description
private Clustering<SubspaceModel>	DiSH.computeClusters(Relation<V> database, ClusterOrderResult<DiSH.DiSHClusterOrderEntry> clusterOrder) Computes the hierarchical clusters according to the cluster order.
Clustering<SubspaceModel>	SUBCLU.getResult() Returns the result of the algorithm.
Clustering<SubspaceModel>	DOC.run(Database database, Relation<V> relation) Performs the DOC or FastDOC (as configured) algorithm on the given Database.
Clustering<SubspaceModel>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel>	P3C.run(Database database, Relation<V> relation) Performs the P3C algorithm on the given Database.
Clustering<SubspaceModel>	DiSH.run(Relation<V> relation) Performs the DiSH algorithm on the given database.
Clustering<SubspaceModel>	SUBCLU.run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
Clustering<SubspaceModel>	CLIQUE.run(Relation<V> relation) Performs the CLIQUE algorithm on the given database.

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

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

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial that return Clustering

Modifier and Type	Method and Description
Clustering<Model>	ByLabelHierarchicalClustering.run(Database database)
Clustering<Model>	ByLabelClustering.run(Database database)
Clustering<Model>	ByLabelOrAllInOneClustering.run(Database database)
Clustering<Model>	ByLabelHierarchicalClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllNoise.run(Relation<?> relation)
Clustering<Model>	ByLabelClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(Relation<?> relation)
Clustering<Model>	ByModelClustering.run(Relation<Model> relation) Run the actual clustering algorithm.

Uses of Clustering in de.lmu.ifi.dbs.elki.datasource.parser

Fields in de.lmu.ifi.dbs.elki.datasource.parser declared as Clustering

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

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

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

Modifier and Type	Method and Description
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringAdjustedRandIndexSimilarityFunction.instantiate(Relation<T> relation)

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

Modifier and Type	Method and Description
SimpleTypeInformation<? super Clustering<?>>	ClusteringAdjustedRandIndexSimilarityFunction.getInputTypeRestriction()

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

Modifier and Type	Method and Description
double	ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)

Uses of Clustering in de.lmu.ifi.dbs.elki.evaluation.clustering

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering with parameters of type Clustering

Modifier and Type	Method and Description
private boolean	EvaluateClustering.isReferenceResult(Clustering<?> t)
void	ClusterContingencyTable.process(Clustering<?> result1, Clustering<?> result2) Process two clustering results.
void	ClusterContingencyTable.process(Clustering<?> result1, Clustering<?> result2) Process two clustering results.

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

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with parameters of type Clustering

Modifier and Type	Method and Description
void	EvaluateSilhouette.evaluateClustering(Database db, Relation<O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.

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

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

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

Constructor parameters in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments with type arguments of type Clustering

Constructor and Description
Segments(List<Clustering<?>> clusterings, HierarchicalResult baseResult) Initialize segments.

Uses of Clustering in de.lmu.ifi.dbs.elki.evaluation.outlier

Methods in de.lmu.ifi.dbs.elki.evaluation.outlier that return Clustering

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

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

Methods in de.lmu.ifi.dbs.elki.result that return types with arguments of type Clustering

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

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

Modifier and Type	Method and Description
protected void	ClusteringVectorDumper.dumpClusteringOutput(PrintStream writer, ResultHierarchy hierarchy, Clustering<?> c) Dump a single clustering result.
private void	KMLOutputHandler.writeClusteringResult(XMLStreamWriter xmlw, Clustering<Model> clustering, Database database)

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

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

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 Clustering in de.lmu.ifi.dbs.elki.result.textwriter.naming

Fields in de.lmu.ifi.dbs.elki.result.textwriter.naming declared as Clustering

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

Constructors in de.lmu.ifi.dbs.elki.result.textwriter.naming with parameters of type Clustering

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

Uses of Clustering in de.lmu.ifi.dbs.elki.visualization

Methods in de.lmu.ifi.dbs.elki.visualization that return Clustering

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

Uses of Clustering in de.lmu.ifi.dbs.elki.visualization.opticsplot

Methods in de.lmu.ifi.dbs.elki.visualization.opticsplot that return Clustering

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

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

Fields in de.lmu.ifi.dbs.elki.visualization.style declared as Clustering

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

Methods in de.lmu.ifi.dbs.elki.visualization.style that return Clustering

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

Constructors in de.lmu.ifi.dbs.elki.visualization.style with parameters of type Clustering

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

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

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.optics declared as Clustering

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

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.optics that return Clustering

Modifier and Type	Method and Description
protected static Clustering<OPTICSModel>	OPTICSClusterVisualization.findOPTICSClustering(Result result) Find the first OPTICS clustering child of a result.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.optics with parameters of type Clustering

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

Uses of Clustering in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster declared as Clustering

Modifier and Type	Field and Description
private Clustering<MeanModel>	ClusterParallelMeanVisualization.Instance.clustering The result we visualize.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster that return Clustering

Modifier and Type	Method and Description
private static Clustering<MeanModel>	ClusterParallelMeanVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster with parameters of type Clustering

Modifier and Type	Method and Description
private static Clustering<MeanModel>	ClusterParallelMeanVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

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

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster declared as Clustering

Modifier and Type	Field and Description
(package private) Clustering<EMModel>	EMClusterVisualization.Instance.clustering The result we work on
(package private) Clustering<Model>	ClusterMeanVisualization.Instance.clustering Clustering to visualize.
(package private) Clustering<Model>	VoronoiVisualization.Instance.clustering The result we work on.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster that return Clustering

Modifier and Type	Method and Description
private static Clustering<MeanModel>	EMClusterVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

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

Modifier and Type	Method and Description
private static Clustering<MeanModel>	EMClusterVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static boolean	ClusterMeanVisualization.testMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static boolean	VoronoiVisualization.testMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

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

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj declared as Clustering

Modifier and Type	Field and Description
private Clustering<Model>	KeyVisualization.Instance.clustering Clustering to display

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

Modifier and Type	Method and Description
private static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c)

Uses of Clustering in tutorial.clustering

Methods in tutorial.clustering that return Clustering

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