Package 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

See Also:

• K-means++ (wikipedia)

Nested Class Summary

Nested Classes

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

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method	Description
List<Cluster<T>>	cluster(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 Details

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 Details

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