Package de.lmu.ifi.dbs.elki.algorithm.clustering

Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.

See:
Description

Interface Summary

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.
OPTICSTypeAlgorithm<D extends Distance<D>>	Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.

Class Summary

AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>>	Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering.Parameterizer	Parameterization class.
AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>	Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
AbstractProjectedDBSCAN.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>	Parameterization class.
DBSCAN<O,D extends Distance<D>>	DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
DBSCAN.Parameterizer<O,D extends Distance<D>>	Parameterization class.
DeLiClu<NV extends NumberVector<NV,?>,D extends Distance<D>>	DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
DeLiClu.Parameterizer<NV extends NumberVector<NV,?>,D extends Distance<D>>	Parameterization class.
EM<V extends NumberVector<V,?>>	Provides the EM algorithm (clustering by expectation maximization).
EM.Parameterizer<V extends NumberVector<V,?>>	Parameterization class.
KMeans<V extends NumberVector<V,?>,D extends Distance<D>>	Provides the k-means algorithm.
KMeans.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>	Parameterization class.
OPTICS<O,D extends Distance<D>>	OPTICS provides the OPTICS algorithm.
OPTICS.Parameterizer<O,D extends Distance<D>>	Parameterization class.
OPTICSXi<N extends NumberDistance<N,?>>	Class to handle OPTICS Xi extraction.
OPTICSXi.Parameterizer<D extends NumberDistance<D,?>>	Parameterization class.
OPTICSXi.SteepArea	Data structure to represent a steep-down-area for the xi method.
OPTICSXi.SteepAreaResult	Result containing the chi-steep areas.
OPTICSXi.SteepDownArea	Data structure to represent a steep-down-area for the xi method.
OPTICSXi.SteepScanPosition<N extends NumberDistance<N,?>>	Position when scanning for steep areas
OPTICSXi.SteepUpArea	Data structure to represent a steep-down-area for the xi method.
SLINK<O,D extends Distance<D>>	Efficient implementation of the Single-Link Algorithm SLINK of R.
SLINK.CompareByLambda<D extends Distance<D>>	Order a DBID collection by the lambda value.
SLINK.Parameterizer<O,D extends Distance<D>>	Parameterization class.
SNNClustering<O>	Shared nearest neighbor clustering.
SNNClustering.Parameterizer<O>	Parameterization class.

Package de.lmu.ifi.dbs.elki.algorithm.clustering Description

Clustering algorithms

Clustering algorithms are supposed to implement the Algorithm-Interface. The more specialized interface ClusteringAlgorithm requires an implementing algorithm to provide a special result class suitable as a partitioning of the database. More relaxed clustering algorithms are allowed to provide a result that is a fuzzy clustering, does not partition the database complete or is in any other sense a relaxed clustering result.

See Also:

de.lmu.ifi.dbs.elki.algorithm