Package org.apache.commons.math3.stat.clustering

Class KMeansPlusPlusClusterer<T extends Clusterable<T>>

java.lang.Object

org.apache.commons.math3.stat.clustering.KMeansPlusPlusClusterer<T>

Type Parameters:

T - type of the points to cluster

@Deprecated
public class KMeansPlusPlusClusterer<T extends Clusterable<T>>
extends java.lang.Object

Deprecated.

As of 3.2 (to be removed in 4.0), use KMeansPlusPlusClusterer instead

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

Since:

2.0

See Also:

K-means++ (wikipedia)

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	KMeansPlusPlusClusterer.EmptyClusterStrategy	Deprecated. Strategies to use for replacing an empty cluster.

Field Summary

Fields

Modifier and Type	Field	Description
private KMeansPlusPlusClusterer.EmptyClusterStrategy	emptyStrategy	Deprecated. Selected strategy for empty clusters.
private java.util.Random	random	Deprecated. Random generator for choosing initial centers.

Constructor Summary

Constructors

Constructor	Description
KMeansPlusPlusClusterer(java.util.Random random)	Deprecated. Build a clusterer.
KMeansPlusPlusClusterer(java.util.Random random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)	Deprecated. Build a clusterer.

Method Summary

Modifier and Type	Method	Description
private static <T extends Clusterable<T>> int	assignPointsToClusters(java.util.List<Cluster<T>> clusters, java.util.Collection<T> points, int[] assignments)	Deprecated. Adds the given points to the closest Cluster.
private static <T extends Clusterable<T>> java.util.List<Cluster<T>>	chooseInitialCenters(java.util.Collection<T> points, int k, java.util.Random random)	Deprecated. Use K-means++ to choose the initial centers.
java.util.List<Cluster<T>>	cluster(java.util.Collection<T> points, int k, int maxIterations)	Deprecated. Runs the K-means++ clustering algorithm.
java.util.List<Cluster<T>>	cluster(java.util.Collection<T> points, int k, int numTrials, int maxIterationsPerTrial)	Deprecated. Runs the K-means++ clustering algorithm.
private T	getFarthestPoint(java.util.Collection<Cluster<T>> clusters)	Deprecated. Get the point farthest to its cluster center
private static <T extends Clusterable<T>> int	getNearestCluster(java.util.Collection<Cluster<T>> clusters, T point)	Deprecated. Returns the nearest Cluster to the given point
private T	getPointFromLargestNumberCluster(java.util.Collection<Cluster<T>> clusters)	Deprecated. Get a random point from the Cluster with the largest number of points
private T	getPointFromLargestVarianceCluster(java.util.Collection<Cluster<T>> clusters)	Deprecated. Get a random point from the Cluster with the largest distance variance.

Methods inherited from class java.lang.Object

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

Field Detail

random

private final java.util.Random random

Deprecated.

Random generator for choosing initial centers.

emptyStrategy

private final KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy

Deprecated.

Selected strategy for empty clusters.

Constructor Detail

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(java.util.Random random)

Deprecated.

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:

random - random generator to use for choosing initial centers

KMeansPlusPlusClusterer

public KMeansPlusPlusClusterer(java.util.Random random,
                               KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)

Deprecated.

Build a clusterer.

Parameters:

random - random generator to use for choosing initial centers

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

Since:

2.2

Method Detail

cluster

public java.util.List<Cluster<T>> cluster(java.util.Collection<T> points,
                                          int k,
                                          int numTrials,
                                          int maxIterationsPerTrial)
                                   throws MathIllegalArgumentException,
                                          ConvergenceException

Deprecated.

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 java.util.List<Cluster<T>> cluster(java.util.Collection<T> points,
                                          int k,
                                          int maxIterations)
                                   throws MathIllegalArgumentException,
                                          ConvergenceException

Deprecated.

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

assignPointsToClusters

private static <T extends Clusterable<T>> int assignPointsToClusters(java.util.List<Cluster<T>> clusters,
                                                                     java.util.Collection<T> points,
                                                                     int[] assignments)

Deprecated.

Adds the given points to the closest Cluster.

Type Parameters:

T - type of the points to 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 static <T extends Clusterable<T>> java.util.List<Cluster<T>> chooseInitialCenters(java.util.Collection<T> points,
                                                                                          int k,
                                                                                          java.util.Random random)

Deprecated.

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

Type Parameters:

T - type of the points to cluster

Parameters:

points - the points to choose the initial centers from

k - the number of centers to choose

random - random generator to use

Returns:

the initial centers

getPointFromLargestVarianceCluster

private T getPointFromLargestVarianceCluster(java.util.Collection<Cluster<T>> clusters)
                                      throws ConvergenceException

Deprecated.

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(java.util.Collection<Cluster<T>> clusters)
                                    throws ConvergenceException

Deprecated.

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(java.util.Collection<Cluster<T>> clusters)
                    throws ConvergenceException

Deprecated.

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 static <T extends Clusterable<T>> int getNearestCluster(java.util.Collection<Cluster<T>> clusters,
                                                                T point)

Deprecated.

Returns the nearest Cluster to the given point

Type Parameters:

T - type of the points to cluster

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