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

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

Access	Constructor and Description
public	KMeansPlusPlusClusterer(final Random random generator to use for choosing initial centers random) Build a clusterer.
public	KMeansPlusPlusClusterer(final Random random generator to use for choosing initial centers random, final KMeansPlusPlusClusterer.EmptyClusterStrategy strategy to use for handling empty clusters that may appear during algorithm iterations emptyStrategy) Build a clusterer.

Method Summary

Modifier and Type	Method and Description
private static < type of the points to cluster T extends Clusterable<T>> int	Returns: the number of points assigned to different clusters as the iteration before assignPointsToClusters(final List<Cluster<T>> the `Cluster`s to add the points to clusters, final Collection<T> the points to add to the given `Cluster`s points, final int[] points assignments to clusters assignments) Adds the given points to the closest `Cluster`.
private static < type of the points to cluster T extends Clusterable<T>> List<Cluster<T>>	Returns: the initial centers chooseInitialCenters(final Collection<T> the points to choose the initial centers from points, final int the number of centers to choose k, final Random random generator to use random) Use K-means++ to choose the initial centers.
public List<Cluster<T>>	Returns: a list of clusters containing the points cluster(final Collection<T> the points to cluster points, final int the number of clusters to split the data into k, int number of trial runs numTrials, int the maximum number of iterations to run the algorithm for at each trial run. If negative, no maximum will be used maxIterationsPerTrial) Runs the K-means++ clustering algorithm.
public List<Cluster<T>>	Returns: a list of clusters containing the points cluster(final Collection<T> the points to cluster points, final int the number of clusters to split the data into k, final int the maximum number of iterations to run the algorithm for. If negative, no maximum will be used maxIterations) Runs the K-means++ clustering algorithm.
private T	Returns: point farthest to its cluster center getFarthestPoint(final Collection<Cluster<T>> the `Cluster`s to search clusters) Get the point farthest to its cluster center
private static < type of the points to cluster T extends Clusterable<T>> int	Returns: the index of the nearest `Cluster` to the given point getNearestCluster(final Collection<Cluster<T>> the `Cluster`s to search clusters, final T the point to find the nearest `Cluster` for point) Returns the nearest `Cluster` to the given point
private T	Returns: a random point from the selected cluster getPointFromLargestNumberCluster(final Collection<Cluster<T>> the `Cluster`s to search clusters) Get a random point from the `Cluster` with the largest number of points
private T	Returns: a random point from the selected cluster getPointFromLargestVarianceCluster(final Collection<Cluster<T>> the `Cluster`s to search clusters) Get a random point from the `Cluster` with the largest distance variance.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

emptyStrategy	back to summary
private final KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy Selected strategy for empty clusters.

random	back to summary
private final Random random Random generator for choosing initial centers.

Constructor Detail

KMeansPlusPlusClusterer	back to summary
public KMeansPlusPlusClusterer(final Random 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 random:Random random generator to use for choosing initial centers

KMeansPlusPlusClusterer

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public KMeansPlusPlusClusterer(final Random 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

random:Random: random generator to use for choosing initial centers

KMeansPlusPlusClusterer	back to summary
public KMeansPlusPlusClusterer(final Random random, final KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy) Build a clusterer. Parameters random:Random random generator to use for choosing initial centers emptyStrategy:KMeansPlusPlusClusterer.EmptyClusterStrategy strategy to use for handling empty clusters that may appear during algorithm iterations Since 2.2

KMeansPlusPlusClusterer

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public KMeansPlusPlusClusterer(final Random random, final KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)

Build a clusterer.

Parameters

random:Random: random generator to use for choosing initial centers
emptyStrategy:KMeansPlusPlusClusterer.EmptyClusterStrategy: strategy to use for handling empty clusters that may appear during algorithm iterations

Since

2.2

Method Detail

assignPointsToClusters back to summary

assignPointsToClusters	back to summary
private static <T extends Clusterable<T>> int assignPointsToClusters(final List<Cluster<T>> clusters, final Collection<T> points, final int[] assignments) Adds the given points to the closest `Cluster`. Parameters <T> type of the points to cluster clusters:List<Cluster<T>> the `Cluster`s to add the points to points:Collection<T> the points to add to the given `Cluster`s assignments:int[] points assignments to clusters Returns:int the number of points assigned to different clusters as the iteration before

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

Adds the given points to the closest Cluster.

Parameters

<T>: type of the points to cluster
clusters:List<Cluster<T>>: the Clusters to add the points to
points:Collection<T>: the points to add to the given Clusters
assignments:int[]: points assignments to clusters

Returns:int

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

chooseInitialCenters	back to summary
private static <T extends Clusterable<T>> List<Cluster<T>> chooseInitialCenters(final Collection<T> points, final int k, final Random random) Use K-means++ to choose the initial centers. Parameters <T> type of the points to cluster points:Collection<T> the points to choose the initial centers from k:int the number of centers to choose random:Random random generator to use Returns:List<Cluster<T>> the initial centers

chooseInitialCenters

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private static <T extends Clusterable<T>> List<Cluster<T>> chooseInitialCenters(final Collection<T> points, final int k, final Random random)

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

Parameters

<T>: type of the points to cluster
points:Collection<T>: the points to choose the initial centers from
k:int: the number of centers to choose
random:Random: random generator to use

Returns:List<Cluster<T>>

the initial centers

cluster back to summary

cluster	back to summary
public List<Cluster<T>> cluster(final Collection<T> points, final int k, int numTrials, int maxIterationsPerTrial) throws MathIllegalArgumentException, ConvergenceException Runs the K-means++ clustering algorithm. Parameters points:Collection<T> the points to cluster k:int the number of clusters to split the data into numTrials:int number of trial runs maxIterationsPerTrial:int the maximum number of iterations to run the algorithm for at each trial run. If negative, no maximum will be used Returns:List<Cluster<T>> a list of clusters containing the points Exceptions 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`

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

Runs the K-means++ clustering algorithm.

Parameters

points:Collection<T>: the points to cluster
k:int: the number of clusters to split the data into
numTrials:int: number of trial runs
maxIterationsPerTrial:int: the maximum number of iterations to run the algorithm for at each trial run. If negative, no maximum will be used

Returns:List<Cluster<T>>

a list of clusters containing the points

Exceptions

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 back to summary

cluster	back to summary
public List<Cluster<T>> cluster(final Collection<T> points, final int k, final int maxIterations) throws MathIllegalArgumentException, ConvergenceException Runs the K-means++ clustering algorithm. Parameters points:Collection<T> the points to cluster k:int the number of clusters to split the data into maxIterations:int the maximum number of iterations to run the algorithm for. If negative, no maximum will be used Returns:List<Cluster<T>> a list of clusters containing the points Exceptions 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`

public List<Cluster<T>> cluster(final Collection<T> points, final int k, final int maxIterations) throws MathIllegalArgumentException, ConvergenceException

Runs the K-means++ clustering algorithm.

Parameters

points:Collection<T>: the points to cluster
k:int: the number of clusters to split the data into
maxIterations:int: the maximum number of iterations to run the algorithm for. If negative, no maximum will be used

Returns:List<Cluster<T>>

a list of clusters containing the points

Exceptions

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

getFarthestPoint back to summary

getFarthestPoint	back to summary
private T getFarthestPoint(final Collection<Cluster<T>> clusters) throws ConvergenceException Get the point farthest to its cluster center Parameters clusters:Collection<Cluster<T>> the `Cluster`s to search Returns:T point farthest to its cluster center Exceptions ConvergenceException: if clusters are all empty

private T getFarthestPoint(final Collection<Cluster<T>> clusters) throws ConvergenceException

Get the point farthest to its cluster center

Parameters

clusters:Collection<Cluster<T>>: the Clusters to search

Returns:T

point farthest to its cluster center

Exceptions

ConvergenceException:: if clusters are all empty

getNearestCluster back to summary

getNearestCluster	back to summary
private static <T extends Clusterable<T>> int getNearestCluster(final Collection<Cluster<T>> clusters, final T point) Returns the nearest `Cluster` to the given point Parameters <T> type of the points to cluster clusters:Collection<Cluster<T>> the `Cluster`s to search point:T the point to find the nearest `Cluster` for Returns:int the index of the nearest `Cluster` to the given point

private static <T extends Clusterable<T>> int getNearestCluster(final Collection<Cluster<T>> clusters, final T point)

Returns the nearest Cluster to the given point

Parameters

<T>: type of the points to cluster
clusters:Collection<Cluster<T>>: the Clusters to search
point:T: the point to find the nearest Cluster for

Returns:int

the index of the nearest Cluster to the given point

getPointFromLargestNumberCluster back to summary

getPointFromLargestNumberCluster	back to summary
private T getPointFromLargestNumberCluster(final Collection<Cluster<T>> clusters) throws ConvergenceException Get a random point from the `Cluster` with the largest number of points Parameters clusters:Collection<Cluster<T>> the `Cluster`s to search Returns:T a random point from the selected cluster Exceptions ConvergenceException: if clusters are all empty

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

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

Parameters

clusters:Collection<Cluster<T>>: the Clusters to search

Returns:T

a random point from the selected cluster

Exceptions

ConvergenceException:: if clusters are all empty

getPointFromLargestVarianceCluster back to summary

getPointFromLargestVarianceCluster	back to summary
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) throws ConvergenceException Get a random point from the `Cluster` with the largest distance variance. Parameters clusters:Collection<Cluster<T>> the `Cluster`s to search Returns:T a random point from the selected cluster Exceptions ConvergenceException: if clusters are all empty

private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) throws ConvergenceException

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

Parameters

clusters:Collection<Cluster<T>>: the Clusters to search

Returns:T

a random point from the selected cluster

Exceptions

ConvergenceException:: if clusters are all empty

org.apache.commons.math3.stat.clustering back to summary

public final Enum KMeansPlusPlusClusterer.EmptyClusterStrategy

Class Inheritance

extends Enum<KMeansPlusPlusClusterer.EmptyClusterStrategy>

java.lang.Object
java.lang.Enum
org.apache.commons.math3.stat.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy

Strategies to use for replacing an empty cluster.

Field Summary

Modifier and Type	Field and Description
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy	ERROR Generate an error.
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy	FARTHEST_POINT Create a cluster around the point farthest from its centroid.
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy	LARGEST_POINTS_NUMBER Split the cluster with largest number of points.
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy	LARGEST_VARIANCE Split the cluster with largest distance variance.

Constructor Summary

Access	Constructor and Description
private	EmptyClusterStrategy()

Method Summary

Modifier and Type	Method and Description
public static KMeansPlusPlusClusterer.EmptyClusterStrategy	valueOf(String name)
public static KMeansPlusPlusClusterer.EmptyClusterStrategy[]	values()

Inherited from java.lang.Enum:: clone compareTo describeConstable equals finalize getDeclaringClass hashCode name ordinal toString valueOf

Field Detail

ERROR	back to summary
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy ERROR Generate an error.

FARTHEST_POINT	back to summary
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy FARTHEST_POINT Create a cluster around the point farthest from its centroid.

LARGEST_POINTS_NUMBER	back to summary
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy LARGEST_POINTS_NUMBER Split the cluster with largest number of points.

LARGEST_VARIANCE	back to summary
public static final KMeansPlusPlusClusterer.EmptyClusterStrategy LARGEST_VARIANCE Split the cluster with largest distance variance.

Constructor Detail

EmptyClusterStrategy	back to summary
private EmptyClusterStrategy()

Method Detail

valueOf	back to summary
public static KMeansPlusPlusClusterer.EmptyClusterStrategy valueOf(String name)

values	back to summary
public static KMeansPlusPlusClusterer.EmptyClusterStrategy[] values()

public Class KMeansPlusPlusClusterer<T extends Clusterable<T>>

Nested and Inner Type Summary

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail

public final Enum KMeansPlusPlusClusterer.EmptyClusterStrategy

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail