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

Packages that use Clustering

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.correlation	Correlation 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.evaluation.paircounting	Evaluation of clustering results via pair counting.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
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.visualizers.optics	Visualizers that do work on OPTICS plots
de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d	Visualizers based on 1D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d	Visualizers based on 2D projections.

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

class	AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
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

private Clustering<Model>	SLINK.extractClusters_erich(DBIDs ids, DataStore<DBID> pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrderResult<N> clusterOrderResult, Relation<?> relation, double ixi, int minpts) Extract clusters from a cluster order result.
private Clustering<DendrogramModel<D>>	SLINK.extractClusters(DBIDs ids, DataStore<DBID> pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<Model>	DBSCAN.run(Database database, Relation<O> relation) Performs the DBSCAN algorithm on the given database.
Clustering<MeanModel<V>>	KMeans.run(Database database, Relation<V> relation) Run k-means
Clustering<Model>	AbstractProjectedDBSCAN.run(Database database, Relation<V> relation)
Clustering<EMModel<V>>	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.correlation

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

protected Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.Parameterizer.algC
private Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.partitionAlgorithm Get the algorithm to run on each partition.

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

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>	CASH.run(Database database, Relation<ParameterizationFunction> relation) Run CASH on the relation.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	COPAC.run(Relation<V> relation) Performs the COPAC algorithm on the given database.
Clustering<CorrelationModel<V>>	ERiC.run(Relation<V> relation) Performs the ERiC algorithm on the given database.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Relation<V> relation, Map<Integer,DBIDs> partitionMap, DistanceQuery<V,D> query) Runs the partition algorithm and creates the result.

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

ClusteringAlgorithm<Clustering<Model>>

COPAC.getPartitionAlgorithm(DistanceQuery<V,D> query) Returns the partition algorithm.

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

private SortedMap<Integer,List<Cluster<CorrelationModel<V>>>>

ERiC.extractCorrelationClusters(Clustering<Model> copacResult, Relation<V> database, int dimensionality) 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.

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

COPAC(FilteredLocalPCABasedDistanceFunction<V,?,D> partitionDistanceFunction, Class<? extends ClusteringAlgorithm<Clustering<Model>>> partitionAlgorithm, Collection<Pair<OptionID,Object>> partitionAlgorithmParameters)
Constructor.

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

private Clustering<SubspaceModel<V>>

SUBCLU.result Holds the result;

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

private Clustering<SubspaceModel<V>>	DiSH.computeClusters(Relation<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder, DiSHDistanceFunction.Instance<V> distFunc) Computes the hierarchical clusters according to the cluster order.
Clustering<SubspaceModel<V>>	SUBCLU.getResult() Returns the result of the algorithm.
Clustering<SubspaceModel<V>>	DiSH.run(Database database, Relation<V> relation) Performs the DiSH algorithm on the given database.
Clustering<Model>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel<V>>	SUBCLU.run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
Clustering<SubspaceModel<V>>	CLIQUE.run(Relation<V> relation) Performs the CLIQUE algorithm on the given database.

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

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

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

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

static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> Triple<Integer,Integer,Integer>	PairCountingFMeasure.countPairs(R result1, S result2) Compare two sets of generated pairs.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> Triple<Integer,Integer,Integer>	PairCountingFMeasure.countPairs(R result1, S result2) Compare two sets of generated pairs.
static <R extends Clustering<M>,M extends Model> PairSortedGeneratorInterface	PairCountingFMeasure.getPairGenerator(R clusters, boolean noiseSpecial, boolean hierarchicalSpecial) Get a pair generator for the given Clustering

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

static List<Clustering<? extends Model>>

ResultUtil.getClusteringResults(Result r) Collect all clustering results from a Result

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

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

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

private Clustering<Model>	VisualizerContext.generateDefaultClustering() Generate a default (fallback) clustering.
Clustering<Model>	VisualizerContext.getOrCreateDefaultClustering() Convenience method to get the clustering to use, and fall back to a default "clustering".

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

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

static <D extends Distance<D>> Clustering<Model>

OPTICSCut.makeOPTICSCut(ClusterOrderResult<D> co, OPTICSDistanceAdapter<D> adapter, double epsilon) Compute an OPTICS cut clustering

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

OPTICSColorFromClustering(ColorLibrary colors, Clustering<?> refc)
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

(package private) Clustering<OPTICSModel>

OPTICSClusterVisualization.clus Our clustering

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

protected static Clustering<OPTICSModel>

OPTICSClusterVisualization.findOPTICSClustering(Result result) Find the first OPTICS clustering child of a result.

Uses of Clustering in de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d declared as Clustering

private Clustering<Model>

P1DHistogramVisualizer.clustering The clustering we visualize

Uses of Clustering in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d declared as Clustering

(package private) Clustering<MeanModel<NV>>	ClusterMeanVisualization.clustering Clustering to visualize.
private Clustering<Model>	ClusteringVisualization.clustering The result we visualize
(package private) Clustering<EMModel<NV>>	EMClusterVisualization.clustering The result we work on
(package private) Clustering<Model>	ClusterConvexHullVisualization.clustering The result we work on

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d that return Clustering

private static <NV extends NumberVector<NV,?>> Clustering<MeanModel<NV>>	ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static <NV extends NumberVector<NV,?>> Clustering<MeanModel<NV>>	EMClusterVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d with parameters of type Clustering

private static <NV extends NumberVector<NV,?>> Clustering<MeanModel<NV>>	ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static <NV extends NumberVector<NV,?>> Clustering<MeanModel<NV>>	EMClusterVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private void	BubbleVisualization.setupCSS(SVGPlot svgp, Clustering<? extends Model> clustering) Registers the Bubble-CSS-Class at a SVGPlot.