Clusterer< T extends Clusterable< T > > Class Reference

Collaboration diagram for Clusterer< T extends Clusterable< T > >:

List of all members.

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Detailed Description

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

Parameters:

<T>	type of the points to cluster

See also:

K-means++ (wikipedia)

Version:

Revision:

811685

Date:

2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009)

Since:

2.0

Public Methods
	Clusterer (final Random random, KMeansClustererComponent component)
List< Cluster< T > >	cluster (final List< T > points, final int k, final int maxIterations) throws InterruptedException, ExecutionException
Package Functions
private< T extends Clusterable < T > > void	assignPointsToClusters (final Collection< Cluster< T >> clusters, final List< T > points) throws InterruptedException, ExecutionException
private< T extends Clusterable < T > > List< Cluster< T > >	chooseInitialCenters (final Collection< T > points, final int k, final Random random)
private< T extends Clusterable < T > > Cluster< T >	getNearestCluster (final Collection< Cluster< T >> clusters, final T point)

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Constructors

Clusterer	(	final Random	random,
		KMeansClustererComponent	component
	)

Build a clusterer.

Parameters:

random	random generator to use for choosing initial centers
kMeansClusterer

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Method Details

private<T extends Clusterable<T> > void assignPointsToClusters	(	final Collection< Cluster< T >>	clusters,
		final List< T >	points
	)		throws InterruptedException, ExecutionException [package]

Adds the given points to the closest Cluster.

Parameters:

<T>	type of the points to cluster
clusters	the Clusters to add the points to
points	the points to add to the given Clusters

Exceptions:

ExecutionException
InterruptedException

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

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

Parameters:

<T>	type of the points to cluster
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

Exceptions:

ExecutionException
InterruptedException

List<Cluster<T> > cluster	(	final List< T >	points,
		final int	k,
		final int	maxIterations
	)		throws InterruptedException, ExecutionException

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

Exceptions:

ExecutionException
InterruptedException

private<T extends Clusterable<T> > Cluster<T> getNearestCluster	(	final Collection< Cluster< T >>	clusters,
		final T	point
	)		[package]

Returns the nearest Cluster to the given point

Parameters:

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

Returns:

the nearest Cluster to the given point