org.apache.commons.math.stat.clustering
Class KMeansPlusPlusClusterer<T extends Clusterable<T>>

java.lang.Object
  org.apache.commons.math.stat.clustering.KMeansPlusPlusClusterer<T>

Type Parameters:

T - type of the points to cluster

public class KMeansPlusPlusClusterer<T extends Clusterable<T>>
extends Object

Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.

Since:

2.0

Version:

$Revision: 1054333 $ $Date: 2011-01-02 01:34:58 +0100 (dim. 02 janv. 2011) $

See Also:

K-means++ (wikipedia)

Nested Class Summary

static class

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

Constructor Summary

KMeansPlusPlusClusterer(Random random)
Build a clusterer.

KMeansPlusPlusClusterer(Random random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)
Build a clusterer.

Method Summary

List<Cluster<T>>

cluster(Collection<T> points, int k, int maxIterations) Runs the K-means++ clustering algorithm.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(Random random)

Build a clusterer.

The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.

Parameters:

random - random generator to use for choosing initial centers

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(Random random,
                               KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)

Build a clusterer.

Parameters:

random - random generator to use for choosing initial centers

emptyStrategy - strategy to use for handling empty clusters that may appear during algorithm iterations

Since:

2.2

Method Detail

cluster

public List<Cluster<T>> cluster(Collection<T> points,
                                int k,
                                int maxIterations)

Runs the K-means++ clustering algorithm.

Parameters:

points - the points to cluster

k - the number of clusters to split the data into

maxIterations - the maximum number of iterations to run the algorithm for. If negative, no maximum will be used

Returns:

a list of clusters containing the points