Package elki.clustering

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:

elki.algorithm

Interface Summary

Interface	Description
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.

Class Summary

Class	Description
AbstractProjectedClustering<R extends Clustering<?>>	Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering.Par	Parameterization class.
BetulaLeafPreClustering	BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
BetulaLeafPreClustering.Par	Parameterization class.
CanopyPreClustering<O>	Canopy pre-clustering is a simple preprocessing step for clustering.
CFSFDP<O>	Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.
CFSFDP.Par<O>	Parameterizer
ClusteringAlgorithmUtil	Utility functionality for writing clustering algorithms.
Leader<O>	Leader clustering algorithm.
NaiveMeanShiftClustering<V extends NumberVector>	Mean-shift based clustering algorithm.
SNNClustering<O>	Shared nearest neighbor clustering.