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

Class MultiKMeansPlusPlusClusterer<T extends Clusterable>

java.lang.Object

org.apache.commons.math4.legacy.ml.clustering.Clusterer<T>

org.apache.commons.math4.legacy.ml.clustering.MultiKMeansPlusPlusClusterer<T>

Type Parameters:

T - type of the points to cluster

public class MultiKMeansPlusPlusClusterer<T extends Clusterable>
extends Clusterer<T>

A wrapper around a k-means++ clustering algorithm which performs multiple trials and returns the best solution.

Since:

3.2

Constructor Summary

Constructors

Constructor	Description
MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T> clusterer, int numTrials)	Build a clusterer.
MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T> clusterer, int numTrials, ClusterRanking evaluator)	Build a clusterer.

Method Summary

Modifier and Type	Method	Description
List<CentroidCluster<T>>	cluster(Collection<T> points)	Runs the K-means++ clustering algorithm.

Methods inherited from class org.apache.commons.math4.legacy.ml.clustering.Clusterer

distance, getDistanceMeasure

Methods inherited from class java.lang.Object

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

Constructor Detail

MultiKMeansPlusPlusClusterer

public MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T> clusterer,
                                    int numTrials)

Build a clusterer.

Parameters:

clusterer - the k-means clusterer to use

numTrials - number of trial runs

MultiKMeansPlusPlusClusterer

public MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T> clusterer,
                                    int numTrials,
                                    ClusterRanking evaluator)

Build a clusterer.

Parameters:

clusterer - the k-means clusterer to use

numTrials - number of trial runs

evaluator - the cluster evaluator to use

Since:

3.3

Method Detail

cluster

public List<CentroidCluster<T>> cluster(Collection<T> points)

Runs the K-means++ clustering algorithm.

Specified by:

cluster in class Clusterer<T extends Clusterable>

Parameters:

points - the points to cluster

Returns:

a list of clusters containing the points

Throws:

MathIllegalArgumentException - if the data points are null or the number of clusters is larger than the number of data points

ConvergenceException - if an empty cluster is encountered and the underlying KMeansPlusPlusClusterer has its KMeansPlusPlusClusterer.EmptyClusterStrategy is set to ERROR.