fr.cnes.sirius.patrius.math.stat.clustering

Class KMeansPlusPlusClusterer<T extends Clusterable<T>>

java.lang.Object

fr.cnes.sirius.patrius.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:

$Id: KMeansPlusPlusClusterer.java 18108 2017-10-04 06:45:27Z bignon $

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(Random randomIn) Build a clusterer.
KMeansPlusPlusClusterer(Random randomIn, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategyIn) Build a clusterer.

Method Summary

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

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:

randomIn - random generator to use for choosing initial centers

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(Random randomIn,
                               KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategyIn)

Build a clusterer.

Parameters:

randomIn - random generator to use for choosing initial centers

emptyStrategyIn - 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 numTrials,
                                int maxIterationsPerTrial)

Runs the K-means++ clustering algorithm.

Parameters:

points - the points to cluster

k - the number of clusters to split the data into

numTrials - number of trial runs

maxIterationsPerTrial - the maximum number of iterations to run the algorithm for at each trial run. If negative, no maximum will be used

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 emptyStrategy is set to ERROR

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

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 emptyStrategy is set to ERROR