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)

Nested Class Summary

static class

KMeansPlusPlusClusterer.EmptyClusterStrategy Strategies to use for replacing an empty cluster.

Field Summary

private KMeansPlusPlusClusterer.EmptyClusterStrategy	emptyStrategy Selected strategy for empty clusters.
private int	k The number of clusters.
private int	maxIterations The maximum number of iterations.
private RandomGenerator	random Random generator for choosing initial centers.

Constructor Summary

KMeansPlusPlusClusterer(int k)
Build a clusterer.

KMeansPlusPlusClusterer(int k, int maxIterations)
Build a clusterer.

KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure)
Build a clusterer.

KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random)
Build a clusterer.

KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)
Build a clusterer.

Method Summary

private int	assignPointsToClusters(List<CentroidCluster<T>> clusters, Collection<T> points, int[] assignments) Adds the given points to the closest Cluster.
private Clusterable	centroidOf(Collection<T> points, int dimension) Computes the centroid for a set of points.
private List<CentroidCluster<T>>	chooseInitialCenters(Collection<T> points) Use K-means++ to choose the initial centers.
List<CentroidCluster<T>>	cluster(Collection<T> points) Runs the K-means++ clustering algorithm.
KMeansPlusPlusClusterer.EmptyClusterStrategy	getEmptyClusterStrategy() Returns the KMeansPlusPlusClusterer.EmptyClusterStrategy used by this instance.
private T	getFarthestPoint(Collection<CentroidCluster<T>> clusters) Get the point farthest to its cluster center
int	getK() Return the number of clusters this instance will use.
int	getMaxIterations() Returns the maximum number of iterations this instance will use.
private int	getNearestCluster(Collection<CentroidCluster<T>> clusters, T point) Returns the nearest Cluster to the given point
private T	getPointFromLargestNumberCluster(Collection<? extends Cluster<T>> clusters) Get a random point from the Cluster with the largest number of points
private T	getPointFromLargestVarianceCluster(Collection<CentroidCluster<T>> clusters) Get a random point from the Cluster with the largest distance variance.
RandomGenerator	getRandomGenerator() Returns the random generator this instance will use.

Methods inherited from class org.apache.commons.math3.ml.clustering.Clusterer

distance, getDistanceMeasure

Methods inherited from class java.lang.Object

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

Field Detail

k

private final int k

The number of clusters.

maxIterations

private final int maxIterations

The maximum number of iterations.

random

private final RandomGenerator random

Random generator for choosing initial centers.

emptyStrategy

private final KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy

Selected strategy for empty clusters.

Constructor Detail

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

Method Detail

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

assignPointsToClusters

private int assignPointsToClusters(List<CentroidCluster<T>> clusters,
                                   Collection<T> points,
                                   int[] assignments)

Adds the given points to the closest Cluster.

Parameters:

clusters - the Clusters to add the points to

points - the points to add to the given Clusters

assignments - points assignments to clusters

Returns:

the number of points assigned to different clusters as the iteration before

chooseInitialCenters

private List<CentroidCluster<T>> chooseInitialCenters(Collection<T> points)

Use K-means++ to choose the initial centers.

Parameters:

points - the points to choose the initial centers from

Returns:

the initial centers

getPointFromLargestVarianceCluster

private T getPointFromLargestVarianceCluster(Collection<CentroidCluster<T>> clusters)
                                                          throws ConvergenceException

Get a random point from the Cluster with the largest distance variance.

Parameters:

clusters - the Clusters to search

Returns:

a random point from the selected cluster

Throws:

ConvergenceException - if clusters are all empty

getPointFromLargestNumberCluster

private T getPointFromLargestNumberCluster(Collection<? extends Cluster<T>> clusters)
                                                        throws ConvergenceException

Get a random point from the Cluster with the largest number of points

Parameters:

clusters - the Clusters to search

Returns:

a random point from the selected cluster

Throws:

ConvergenceException - if clusters are all empty

getFarthestPoint

private T getFarthestPoint(Collection<CentroidCluster<T>> clusters)
                                        throws ConvergenceException

Get the point farthest to its cluster center

Parameters:

clusters - the Clusters to search

Returns:

point farthest to its cluster center

Throws:

ConvergenceException - if clusters are all empty

getNearestCluster

private int getNearestCluster(Collection<CentroidCluster<T>> clusters,
                              T point)

Returns the nearest Cluster to the given point

Parameters:

clusters - the Clusters to search

point - the point to find the nearest Cluster for

Returns:

the index of the nearest Cluster to the given point

centroidOf

private Clusterable centroidOf(Collection<T> points,
                               int dimension)

Computes the centroid for a set of points.

Parameters:

points - the set of points

dimension - the point dimension

Returns:

the computed centroid for the set of points