de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Class SampleKMeansInitialization<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.AbstractKMeansInitialization<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.SampleKMeansInitialization<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

KMeansInitialization<V>

public class SampleKMeansInitialization<V extends NumberVector>
extends AbstractKMeansInitialization<V>

Initialize k-means by running k-means on a sample of the data set only.

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SampleKMeansInitialization.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private KMeans<V,?>	innerkMeans Variant of kMeans to use for initialization.
private double	rate Sample size.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.AbstractKMeansInitialization

rnd

Constructor Summary

Constructors

Constructor and Description
SampleKMeansInitialization(RandomFactory rnd, KMeans<V,?> innerkMeans, double rate) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
<T extends V,O extends NumberVector> List<O>	chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory) Choose initial means

Methods inherited from class java.lang.Object

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

Field Detail

innerkMeans

private KMeans<V extends NumberVector,?> innerkMeans

Variant of kMeans to use for initialization.

rate

private double rate

Sample size.

Constructor Detail

SampleKMeansInitialization

public SampleKMeansInitialization(RandomFactory rnd,
                          KMeans<V,?> innerkMeans,
                          double rate)

Constructor.

Parameters:

rnd - Random generator.

innerkMeans - Inner k-means algorithm.

rate - Sampling rate.

Method Detail

chooseInitialMeans

public <T extends V,O extends NumberVector> List<O> chooseInitialMeans(Database database,
                                                              Relation<T> relation,
                                                              int k,
                                                              NumberVectorDistanceFunction<? super T> distanceFunction,
                                                              NumberVector.Factory<O> factory)

Description copied from interface: KMeansInitialization

Choose initial means

Type Parameters:

T - Input vector type

O - Output vector type

Parameters:

database - Database context

relation - Relation

k - Parameter k

distanceFunction - Distance function

factory - Factory for output vectors.

Returns:

List of chosen means for k-means