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.data.cluster.naming	Naming schemes for clusters (for output when an algorithm doesn't generate cluster names).
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
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.visualization.opticsplot	Code for drawing OPTICS plots
de.lmu.ifi.dbs.elki.visualization.visualizers	Visualizers for various results
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d	Visualizers based on 2D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj	Visualizers that do not use a particular projection.

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

interface

ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject> Interface for Algorithms that are capable to provide a Clustering as Result.

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

protected Clustering<Model>	SNNClustering.runInTime(Database<O> database) Performs the SNN clustering algorithm on the given database.
protected Clustering<Model>	ByLabelClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	DBSCAN.runInTime(Database<O> database) Performs the DBSCAN algorithm on the given database.
protected Clustering<Model>	TrivialAllInOne.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	TrivialAllNoise.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ByLabelHierarchicalClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ProjectedDBSCAN.runInTime(Database<V> database)
protected Clustering<EMModel<V>>	EM.runInTime(Database<V> database) Performs the EM clustering algorithm on the given database.
protected Clustering<MeanModel<V>>	KMeans.runInTime(Database<V> database) Performs the k-means 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 ObjectParameter<ClusteringAlgorithm<Clustering<Model>,V>>	COPAC.PARTITION_ALGORITHM_PARAM Parameter to specify the clustering algorithm to apply to each partition, must extend ClusteringAlgorithm.
private ClusteringAlgorithm<Clustering<Model>,V>	COPAC.partitionAlgorithm Holds the instance of the partitioning algorithm specified by COPAC.PARTITION_ALGORITHM_PARAM.

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

private Clustering<Model>	CASH.doRun(Database<ParameterizationFunction> database, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
protected Clustering<Model>	CASH.runInTime(Database<ParameterizationFunction> database) Performs the CASH algorithm on the given database.
protected Clustering<CorrelationModel<V>>	ERiC.runInTime(Database<V> database) Performs the ERiC algorithm on the given database.
protected Clustering<Model>	ORCLUS.runInTime(Database<V> database) Performs the ORCLUS algorithm on the given database.
protected Clustering<Model>	COPAC.runInTime(Database<V> database) Performs the COPAC algorithm on the given database.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap) 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>,V>

COPAC.getPartitionAlgorithm() 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, Database<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.

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(Database<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder) Computes the hierarchical clusters according to the cluster order.
Clustering<SubspaceModel<V>>	SUBCLU.getResult() Returns the result of the algorithm.
protected Clustering<SubspaceModel<V>>	DiSH.runInTime(Database<V> database) Performs the DiSH algorithm on the given database.
protected Clustering<Model>	PROCLUS.runInTime(Database<V> database) Performs the PROCLUS algorithm on the given database.
protected Clustering<SubspaceModel<V>>	CLIQUE.runInTime(Database<V> database) Performs the CLIQUE algorithm on the given database.
protected Clustering<SubspaceModel<V>>	SUBCLU.runInTime(Database<V> database) Performs the SUBCLU algorithm on the given database.

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

Fields in de.lmu.ifi.dbs.elki.data.cluster.naming declared as Clustering

private Clustering<?>

SimpleEnumeratingScheme.clustering Clustering this scheme is applied to.

Constructors in de.lmu.ifi.dbs.elki.data.cluster.naming with parameters of type Clustering

SimpleEnumeratingScheme(Clustering<?> clustering)
Constructor.

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

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

<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Database<O>	LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model> Map<Cluster<M>,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering) Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Map<L,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Use an existing clustering to partition a database.

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, 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> PairSortedGeneratorInterface	PairCountingFMeasure.getPairGenerator(R clusters) 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.visualization.opticsplot

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

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers 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.visualizers.vis2d

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

private Clustering<Model>	BubbleVisualizer.clustering A clustering of the database.
protected Clustering<Model>	ClusteringVisualizer.clustering Clustering to visualize.

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

void	ClusteringVisualizer.init(VisualizerContext context, Clustering<?> clustering) Initializes this Visualizer.

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

private Clustering<Model>

KeyVisualizer.clustering The clustering to visualize

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

void	KeyVisualizer.init(VisualizerContext context, Clustering<?> clustering) Initialization.