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 Clustering algorithms are supposed to implement the Algorithm-Interface.
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 Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel	Parallel versions of Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
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.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.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
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.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file).
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.actions	Action-only "visualizers" that only produce menu entries.
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots
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<PrototypeModel<O>>	Leader.run(Relation<O> relation) Run the leader clustering algorithm.
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(Relation<V> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.Instance.run(Relation<V> 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
protected abstract Clustering<M>	AbstractBiclustering.biclustering() Run the actual biclustering algorithm.
Clustering<BiclusterWithInversionsModel>	ChengAndChurch.biclustering()
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<DimensionModel>	COPAC.run(Database database, Relation<V> relation) Run the COPAC algorithm.
Clustering<CorrelationModel>	ERiC.run(Database database, Relation<V> relation) Performs the ERiC algorithm on the given database.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	CASH.run(Relation<V> rel) Run CASH on the relation.

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> 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, Relation<V> 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 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.gdbscan.parallel

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

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

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

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

Modifier and Type	Method and Description
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation) Run the clustering algorithm.

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

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

Modifier and Type	Method and Description
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildFlat(DBIDArrayIter it, int split, FiniteProgress progress) Build a flat clustering.
private Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.buildHierarchical(DBIDArrayIter it, int split, FiniteProgress progress) Build a hierarchical clustering.
Clustering<DendrogramModel>	AbstractCutDendrogram.Instance.extractClusters() Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.Instance.run() Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.Instance.run() Extract all clusters from the pi-lambda-representation.
Clustering<Model>	ClustersWithNoiseExtraction.Instance.run() Extract all clusters from the pi-lambda-representation.
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.run(Database database)
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.run(Database database)
Clustering<Model>	ClustersWithNoiseExtraction.run(Database database)
Clustering<DendrogramModel>	AbstractCutDendrogram.run(Database database)
Clustering<DendrogramModel>	CutDendrogramByNumberOfClusters.run(PointerHierarchyRepresentationResult pointerresult)
Clustering<DendrogramModel>	CutDendrogramByHeight.run(PointerHierarchyRepresentationResult pointerresult)
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.run(PointerHierarchyRepresentationResult pointerresult) Process an existing result.
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.run(PointerHierarchyRepresentationResult pointerresult) Process an existing result.
Clustering<Model>	ClustersWithNoiseExtraction.run(PointerHierarchyRepresentationResult pointerresult) Process an existing result.
abstract Clustering<DendrogramModel>	AbstractCutDendrogram.run(PointerHierarchyRepresentationResult pointerresult) Process a pointer hierarchy result.

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

Modifier and Type	Method and Description
private void	HDBSCANHierarchyExtraction.Instance.collectChildren(HDBSCANHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, HDBSCANHierarchyExtraction.TempCluster cur, Cluster<DendrogramModel> clus, boolean flatten) Recursive flattening of clusters.
private void	HDBSCANHierarchyExtraction.Instance.finalizeCluster(HDBSCANHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, Cluster<DendrogramModel> parent, boolean flatten) Make the cluster for the given object
protected Cluster<DendrogramModel>	SimplifiedHierarchyExtraction.Instance.toCluster(SimplifiedHierarchyExtraction.TempCluster temp, Clustering<DendrogramModel> clustering, DBIDRef lead) Make the cluster for the given object

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
protected Clustering<MeanModel>	KMediansLloyd.Instance.buildMediansResult()
protected Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult() Build a standard k-means result, with known cluster variance sums.
protected Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult(boolean varstat, Relation<? extends NumberVector> relation) Build the result, recomputing the cluster variance if varstat is set to true.
protected Clustering<KMeansModel>	KMeansMinusMinus.Instance.buildResultWithNoise()
Clustering<M>	BestOfMultipleKMeans.run(Database database, Relation<V> relation)
Clustering<M>	KMeans.run(Database database, Relation<V> rel) Run the clustering algorithm.
Clustering<MedoidModel>	FastCLARA.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	SingleAssignmentKMeans.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansSort.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansAnnulus.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansExponion.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansLloyd.run(Database database, Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansElkan.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansHamerly.run(Database database, Relation<V> relation)
Clustering<M>	KMeansBisecting.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMinusMinus.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsPAM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<MedoidModel>	KMedoidsPark.run(Database database, Relation<V> relation) Run k-medoids
Clustering<KMeansModel>	KMeansSimplifiedElkan.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansCompare.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARANS.run(Database database, Relation<V> relation)
Clustering<M>	XMeans.run(Database database, Relation<V> relation) Run the algorithm on a database and relation.
Clustering<MedoidModel>	FastCLARANS.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 <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> 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	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.

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

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

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

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(ClusterOrder 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

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, DiSH.DiSHClusterOrder clusterOrder) Computes the hierarchical clusters according to the cluster order.
Clustering<SubspaceModel>	DiSH.run(Database db, Relation<V> relation) Performs the DiSH algorithm on the given database.
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>	CLIQUE.run(Relation<? extends NumberVector> relation) Performs the CLIQUE algorithm on the given database.
Clustering<SubspaceModel>	SUBCLU.run(Relation<V> relation) Performs the SUBCLU 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, java.util.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.algorithm.clustering.uncertain

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

Modifier and Type	Class and Description
class	CenterOfMassMetaClustering<C extends Clustering<?>> Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.
static class	CenterOfMassMetaClustering.Parameterizer<C extends Clustering<?>> Parameterization class.

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

Modifier and Type	Method and Description
Clustering<?>	RepresentativeUncertainClustering.run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
Clustering<?>	UKMeans.run(Database database, Relation<DiscreteUncertainObject> relation) Run the clustering.
protected Clustering<?>	RepresentativeUncertainClustering.runClusteringAlgorithm(ResultHierarchy hierarchy, Result parent, DBIDs ids, DataStore<DoubleVector> store, int dim, java.lang.String title) Run a clustering algorithm on a single instance.

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

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

Modifier and Type	Field and Description
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.clusteringAlgorithm The clustering algorithm to use.
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.Parameterizer.clusteringAlgorithm The clustering algorithm to use.

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with type arguments of type Clustering

Constructor and Description
CBLOF(NumberVectorDistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta) Constructor.

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

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

Modifier and Type	Field and Description
static SimpleTypeInformation<Clustering<?>>	Clustering.TYPE Type information, for relation matching.

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

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

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>	ClusteringRandIndexSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringFowlkesMallowsSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringDistanceSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringBCubedF1SimilarityFunction.instantiate(Relation<T> relation)
<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<?>>	ClusteringRandIndexSimilarityFunction.getInputTypeRestriction()
SimpleTypeInformation<? super Clustering<?>>	ClusteringFowlkesMallowsSimilarityFunction.getInputTypeRestriction()
SimpleTypeInformation<? super Clustering<?>>	ClusteringBCubedF1SimilarityFunction.getInputTypeRestriction()
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	ClusteringRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1SimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1SimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1, Clustering<?> o2)
double	ClusteringRandIndexSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringRandIndexSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringFowlkesMallowsSimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1SimilarityFunction.similarity(Clustering<?> o1, Clustering<?> o2)
double	ClusteringBCubedF1SimilarityFunction.similarity(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
protected void	EvaluateClustering.evaluteResult(Database db, Clustering<?> c, Clustering<?> refc) Evaluate a clustering result.
protected void	EvaluateClustering.evaluteResult(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.
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
double	EvaluateSquaredErrors.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateConcordantPairs.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateVarianceRatioCriteria.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateSimplifiedSilhouette.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluatePBMIndex.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDaviesBouldin.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateCIndex.evaluateClustering(Database db, Relation<? extends O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDBCV.evaluateClustering(Database db, Relation<O> rel, Clustering<?> cl) Evaluate a single clustering.
double	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 java.util.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(java.util.List<Clustering<?>> clusterings) 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 with parameters of type Clustering

Modifier and Type	Method and Description
protected void	ClusteringVectorDumper.dumpClusteringOutput(java.io.PrintStream writer, ResultHierarchy hierarchy, Clustering<?> c) Dump a single clustering result.
private void	KMLOutputHandler.writeClusteringResult(javax.xml.stream.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, java.util.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 ClusterOrder> Clustering<Model>	OPTICSCut.makeOPTICSCut(E co, 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.actions

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

Modifier and Type	Field and Description
private Clustering<?>	ClusterStyleAction.SetStyleAction.c Clustering to use

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

Constructor and Description
SetStyleAction(Clustering<?> c, VisualizerContext context) 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 with parameters of type Clustering

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

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

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

Modifier and Type	Method and Description
protected static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c) Compute the size of the clustering.

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.