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

Release 0.4.0 (2011-09-20_1324)