gov.sandia.cognition.learning.algorithm.clustering

Class KMeansFactory

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.util.AbstractRandomized

gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory

All Implemented Interfaces:

Factory<ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

@CodeReview(reviewer="Justin Basilico",
            date="2009-06-29",
            changesNeeded=false,
            comments="Changed it to not have a default random, otherwise its good.")
public class KMeansFactory
extends AbstractRandomized
implements Factory<ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>>

Creates a parallelized version of the k-means clustering algorithm for the typical use: clustering vector data with a Euclidean distance metric.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_NUM_CLUSTERS The default number of clusters is 10.

Fields inherited from class gov.sandia.cognition.util.AbstractRandomized

random

Constructor Summary

Constructors

Constructor and Description
KMeansFactory() Creates a new instance of KMeansFactory
KMeansFactory(int numClusters) Creates a new instance of KMeansFactory.
KMeansFactory(int numClusters, java.util.Random random) Creates a new instance of KMeansFactory.

Method Summary

Modifier and Type	Method and Description
ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	create() Creates a new instance of an object.
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	create(int numClusters, java.util.Random random) Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k) and random number generator.
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	create(int numClusters, Semimetric<? super Vector> distanceMetric, java.util.Random random) Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k), distance metric, and random number generator.
int	getNumClusters() Gets the number of clusters for the algorithm to attempt to create.
void	setNumClusters(int numClusters) Sets the number of clusters for the algorithm to attempt to create.

Methods inherited from class gov.sandia.cognition.util.AbstractRandomized

clone, getRandom, setRandom

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_NUM_CLUSTERS

public static final int DEFAULT_NUM_CLUSTERS

The default number of clusters is 10.

See Also:

Constant Field Values

Constructor Detail

KMeansFactory

public KMeansFactory()

Creates a new instance of KMeansFactory

KMeansFactory

public KMeansFactory(int numClusters)

Creates a new instance of KMeansFactory.

Parameters:

numClusters - The number of clusters to use.

KMeansFactory

public KMeansFactory(int numClusters,
                     java.util.Random random)

Creates a new instance of KMeansFactory.

Parameters:

numClusters - The number of clusters to use.

random - The random number generator.

Method Detail

create

public static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> create(int numClusters,
                                                                                 java.util.Random random)

Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k) and random number generator.

Parameters:

numClusters - Number of clusters to use (k).

random - Random number generator. Used in the cluster initialization.

Returns:

k-means clustering algorithm for Vector-based clustering.

create

public static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> create(int numClusters,
                                                                                 Semimetric<? super Vector> distanceMetric,
                                                                                 java.util.Random random)

Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k), distance metric, and random number generator.

Parameters:

numClusters - Number of clusters to use (k).

distanceMetric - The distance metric for vectors to use.

random - Random number generator. Used in the cluster initialization.

Returns:

k-means clustering algorithm for Vector-based clustering.

create

public ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> create()

Description copied from interface: Factory

Creates a new instance of an object.

Specified by:

create in interface Factory<ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>>

Returns:

A newly created object.

getNumClusters

public int getNumClusters()

Gets the number of clusters for the algorithm to attempt to create.

Returns:

The number of clusters.

setNumClusters

public void setNumClusters(int numClusters)

Sets the number of clusters for the algorithm to attempt to create. Must be positive.

Parameters:

numClusters - The number of clusters for the algorithm to attempt to create.