edu.uci.ics.jung.algorithms.util

Class KMeansClusterer<T>

java.lang.Object

edu.uci.ics.jung.algorithms.util.KMeansClusterer<T>

public class KMeansClusterer<T>
extends java.lang.Object

Groups items into a specified number of clusters, based on their proximity in d-dimensional space, using the k-means algorithm. Calls to cluster will terminate when either of the two following conditions is true:

• the number of iterations is > max_iterations
• none of the centroids has moved as much as convergence_threshold since the previous iteration

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansClusterer.NotEnoughClustersException An exception that indicates that the specified data points cannot be clustered into the number of clusters requested by the user.

Constructor Summary

Constructors

Constructor and Description
KMeansClusterer() Creates an instance with max iterations of 100 and convergence threshold of 0.001.
KMeansClusterer(int max_iterations, double convergence_threshold) Creates an instance which will terminate when either the maximum number of iterations has been reached, or all changes are smaller than the convergence threshold.

Method Summary

Modifier and Type	Method and Description
java.util.Collection<java.util.Map<T,double[]>>	cluster(java.util.Map<T,double[]> object_locations, int num_clusters) Returns a Collection of clusters, where each cluster is represented as a Map of Objects to locations in d-dimensional space.
double	getConvergenceThreshold()
int	getMaxIterations()
void	setConvergenceThreshold(double convergence_threshold)
void	setMaxIterations(int max_iterations)
void	setSeed(int random_seed) Sets the seed used by the internal random number generator.

Methods inherited from class java.lang.Object

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

Constructor Detail

KMeansClusterer

public KMeansClusterer(int max_iterations,
                       double convergence_threshold)

Creates an instance which will terminate when either the maximum number of iterations has been reached, or all changes are smaller than the convergence threshold.

Parameters:

max_iterations - the maximum number of iterations to employ

convergence_threshold - the smallest change we want to track

KMeansClusterer

public KMeansClusterer()

Creates an instance with max iterations of 100 and convergence threshold of 0.001.

Method Detail

getMaxIterations

public int getMaxIterations()

Returns:

the maximum number of iterations

setMaxIterations

public void setMaxIterations(int max_iterations)

Parameters:

max_iterations - the maximum number of iterations

getConvergenceThreshold

public double getConvergenceThreshold()

Returns:

the convergence threshold

setConvergenceThreshold

public void setConvergenceThreshold(double convergence_threshold)

Parameters:

convergence_threshold - the convergence threshold

cluster

public java.util.Collection<java.util.Map<T,double[]>> cluster(java.util.Map<T,double[]> object_locations,
                                                               int num_clusters)

Returns a Collection of clusters, where each cluster is represented as a Map of Objects to locations in d-dimensional space.

Parameters:

object_locations - a map of the items to cluster, to double arrays that specify their locations in d-dimensional space.

num_clusters - the number of clusters to create

Returns:

a clustering of the input objects in d-dimensional space

Throws:

KMeansClusterer.NotEnoughClustersException - if num_clusters is larger than the number of distinct points in object_locations

setSeed

public void setSeed(int random_seed)

Sets the seed used by the internal random number generator. Enables consistent outputs.

Parameters:

random_seed - the random seed to use