org.apache.commons.math3.ml.clustering
Class KMeansPlusPlusClusterer<T extends Clusterable>

java.lang.Object
  org.apache.commons.math3.ml.clustering.Clusterer<T>
      org.apache.commons.math3.ml.clustering.KMeansPlusPlusClusterer<T>

Type Parameters:

T - type of the points to cluster

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

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

Since:

3.2

Version:

$Id: KMeansPlusPlusClusterer.java 1461866 2013-03-27 21:54:36Z tn $

See Also:

K-means++ (wikipedia)

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(int k)

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.

The euclidean distance will be used as default distance measure.

Parameters:

k - the number of clusters to split the data into

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(int k,
                               int maxIterations)

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.

The euclidean distance will be used as default distance measure.

Parameters:

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.

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(int k,
                               int maxIterations,
                               DistanceMeasure measure)

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:

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.

measure - the distance measure to use

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(int k,
                               int maxIterations,
                               DistanceMeasure measure,
                               RandomGenerator 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:

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.

measure - the distance measure to use

random - random generator to use for choosing initial centers

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(int k,
                               int maxIterations,
                               DistanceMeasure measure,
                               RandomGenerator random,
                               KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)

Build a clusterer.

Parameters:

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.

measure - the distance measure to use

random - random generator to use for choosing initial centers

emptyStrategy - strategy to use for handling empty clusters that may appear during algorithm iterations

getK

public int getK()

Return the number of clusters this instance will use.

Returns:

the number of clusters

getMaxIterations

public int getMaxIterations()

Returns the maximum number of iterations this instance will use.

Returns:

the maximum number of iterations, or -1 if no maximum is set

getRandomGenerator

public RandomGenerator getRandomGenerator()

Returns the random generator this instance will use.

Returns:

the random generator

getEmptyClusterStrategy

public KMeansPlusPlusClusterer.EmptyClusterStrategy getEmptyClusterStrategy()

Returns the KMeansPlusPlusClusterer.EmptyClusterStrategy used by this instance.

Returns:

the KMeansPlusPlusClusterer.EmptyClusterStrategy

cluster

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

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 emptyStrategy is set to ERROR