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>,O extends DatabaseObject>

Interface for Algorithms that are capable to provide a Clustering as Result.

Class Summary

ByLabelClustering<O extends DatabaseObject>	Pseudo clustering using labels.
ByLabelHierarchicalClustering<O extends DatabaseObject>	Pseudo clustering using labels.
DBSCAN<O extends DatabaseObject,D extends Distance<D>>	DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>>	DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
EM<V extends RealVector<V,?>>	Provides the EM algorithm (clustering by expectation maximization).
KMeans<D extends Distance<D>,V extends RealVector<V,?>>	Provides the k-means algorithm.
OPTICS<O extends DatabaseObject,D extends Distance<D>>	OPTICS provides the OPTICS algorithm.
ProjectedDBSCAN<V extends RealVector<V,?>>	Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
SLINK<O extends DatabaseObject,D extends Distance<D>>	Efficient implementation of the Single-Link Algorithm SLINK of R.
SNNClustering<O extends DatabaseObject,D extends Distance<D>>	Shared nearest neighbor clustering.
TrivialAllInOne<O extends DatabaseObject>	Trivial pseudo-clustering that just considers all points to be one big cluster.
TrivialAllNoise<O extends DatabaseObject>	Trivial pseudo-clustering that just considers all points to be noise.

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