Package org.apache.commons.math4.legacy.ml.clustering

Clustering algorithms.

Interface Summary

Interface	Description
Clusterable	Interface for n-dimensional points that can be clustered together.
ClusterEvaluator	Defines a measure of the quality of clusters.
ClusterRanking	Evaluates the quality of a set of clusters.

Class Summary

Class	Description
CentroidCluster<T extends Clusterable>	A Cluster used by centroid-based clustering algorithms.
Cluster<T extends Clusterable>	Cluster holding a set of Clusterable points.
Clusterer<T extends Clusterable>	Base class for clustering algorithms.
DBSCANClusterer<T extends Clusterable>	DBSCAN (density-based spatial clustering of applications with noise) algorithm.
DoublePoint	A simple implementation of Clusterable for points with double coordinates.
ElkanKMeansPlusPlusClusterer<T extends Clusterable>	Implementation of k-means++ algorithm.
FuzzyKMeansClusterer<T extends Clusterable>	Fuzzy K-Means clustering algorithm.
KMeansPlusPlusClusterer<T extends Clusterable>	Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
MiniBatchKMeansClusterer<T extends Clusterable>	Clustering algorithm based on KMeans.
MultiKMeansPlusPlusClusterer<T extends Clusterable>	A wrapper around a k-means++ clustering algorithm which performs multiple trials and returns the best solution.

Enum Summary

Enum	Description
KMeansPlusPlusClusterer.EmptyClusterStrategy	Strategies to use for replacing an empty cluster.