Package elki.clustering.kmeans

Class BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>

java.lang.Object

elki.clustering.kmeans.BestOfMultipleKMeans<V,M>

Type Parameters:

V - Vector type

M - Model type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<M>>, KMeans<V,M>

public class BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
extends java.lang.Object
implements KMeans<V,M>

Run K-Means multiple times, and keep the best run.

Since:

0.6.0

Author:

Stephan Baier, Erich Schubert

Nested Class Summary

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private KMeans<V,M>	innerkMeans	Variant of kMeans for the bisecting step.
private static Logging	LOG	The logger for this class.
private KMeansQualityMeasure<? super V>	qualityMeasure	Quality measure which should be used.
private int	trials	Number of trials to do.

Fields inherited from interface elki.clustering.kmeans.KMeans

DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors

Constructor	Description
BestOfMultipleKMeans(int trials, KMeans<V,M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure)	Constructor.

Method Summary

Modifier and Type	Method	Description
NumberVectorDistance<? super V>	getDistance()	Returns the distance.
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<M>	run(Relation<V> relation)	Run the clustering algorithm.
void	setDistance(NumberVectorDistance<? super V> distance)	Set the distance function to use.
void	setInitializer(KMeansInitialization init)	Set the initialization method.
void	setK(int k)	Set the value of k.

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

The logger for this class.

trials

private int trials

Number of trials to do.

innerkMeans

private KMeans<V extends NumberVector,M extends MeanModel> innerkMeans

Variant of kMeans for the bisecting step.

qualityMeasure

private KMeansQualityMeasure<? super V extends NumberVector> qualityMeasure

Quality measure which should be used.

Constructor Detail

BestOfMultipleKMeans

public BestOfMultipleKMeans(int trials,
                            KMeans<V,M> innerkMeans,
                            KMeansQualityMeasure<? super V> qualityMeasure)

Constructor.

Parameters:

trials - Number of trials to do.

innerkMeans - K-Means variant to actually use.

qualityMeasure - Quality measure

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

run

public Clustering<M> run(Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

relation - Relation to process.

Returns:

Clustering result

getDistance

public NumberVectorDistance<? super V> getDistance()

Description copied from interface: KMeans

Returns the distance.

Specified by:

getDistance in interface KMeans<V extends NumberVector,M extends MeanModel>

Returns:

the distance

setK

public void setK(int k)

Description copied from interface: KMeans

Set the value of k. Needed for some types of nested k-means.

Specified by:

setK in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

k - K parameter

setDistance

public void setDistance(NumberVectorDistance<? super V> distance)

Description copied from interface: KMeans

Set the distance function to use.

Specified by:

setDistance in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

distance - Distance function.

setInitializer

public void setInitializer(KMeansInitialization init)

Description copied from interface: KMeans

Set the initialization method.

Specified by:

setInitializer in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

init - Initialization method