org.hipparchus.clustering

Class MultiKMeansPlusPlusClusterer<T extends Clusterable>

java.lang.Object

org.hipparchus.clustering.Clusterer<T>

org.hipparchus.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.

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
List<CentroidCluster<T>>	cluster(Collection<T> points) Runs the K-means++ clustering algorithm.
KMeansPlusPlusClusterer<T>	getClusterer() Returns the embedded k-means clusterer used by this instance.
ClusterEvaluator<T>	getClusterEvaluator() Returns the ClusterEvaluator used to determine the "best" clustering.
int	getNumTrials() Returns the number of trials this instance will do.

Methods inherited from class org.hipparchus.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,
                                    ClusterEvaluator<T> evaluator)

Build a clusterer.

Parameters:

clusterer - the k-means clusterer to use

numTrials - number of trial runs

evaluator - the cluster evaluator to use

Method Detail

getClusterer

public KMeansPlusPlusClusterer<T> getClusterer()

Returns the embedded k-means clusterer used by this instance.

Returns:

the embedded clusterer

getNumTrials

public int getNumTrials()

Returns the number of trials this instance will do.

Returns:

the number of trials

getClusterEvaluator

public ClusterEvaluator<T> getClusterEvaluator()

Returns the ClusterEvaluator used to determine the "best" clustering.

Returns:

the used ClusterEvaluator

cluster

public List<CentroidCluster<T>> cluster(Collection<T> points)
                                 throws MathIllegalArgumentException,
                                        MathIllegalStateException

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

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