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 java.lang.Object

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

Since:

2.0

See Also:

K-means++ (wikipedia)

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansPlusPlusClusterer.EmptyClusterStrategy Strategies to use for replacing an empty cluster.

Constructor Summary

Constructors

Constructor and Description
KMeansPlusPlusClusterer(java.util.Random random) Build a clusterer.
KMeansPlusPlusClusterer(java.util.Random random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy) Build a clusterer.

Method Summary

Modifier and Type	Method and Description
java.util.List<Cluster<T>>	cluster(java.util.Collection<T> points, int k, int maxIterations) Runs the K-means++ clustering algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(java.util.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(java.util.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 java.util.List<Cluster<T>> cluster(java.util.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