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

Packages that use Clustering

de.lmu.ifi.dbs.elki.algorithm.clustering	Package collects clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Package to collect correlation clustering algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Package to collect algorithms for clustering in axis-parallel subspaces, suitable as a task for the KDDTask main routine.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement Clustering

class	DBSCAN<O extends DatabaseObject,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	EM<V extends RealVector<V,?>> Provides the EM algorithm (clustering by expectation maximization).
class	KMeans<D extends Distance<D>,V extends RealVector<V,?>> Provides the k-means algorithm.
class	ProjectedDBSCAN<V extends RealVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	SNNClustering<O extends DatabaseObject,D extends Distance<D>> Shared nearest neighbor clustering.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Clustering

class	COPAC<V extends RealVector<V,?>> Algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
class	FourC<O extends RealVector<O,?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	ORCLUS<V extends RealVector<V,?>> ORCLUS provides the ORCLUS algorithm.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Clustering

class	CLIQUE<V extends RealVector<V,?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	PreDeCon<V extends RealVector<V,?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends RealVector<V,?>> PROCLUS provides the PROCLUS algorithm.
class	ProjectedClustering<V extends RealVector<V,?>> Abstract superclass for PROCLUS and ORCLUS.