Package elki.clustering.kmeans

Class BestOfMultipleKMeans<V extends elki.data.NumberVector,M extends MeanModel>

java.lang.Object

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

Type Parameters:

V - Vector type

M - Model type

All Implemented Interfaces:

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

public class BestOfMultipleKMeans<V extends elki.data.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

elki.Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private KMeans<V,M>	innerkMeans	Variant of kMeans for the bisecting step.
private static elki.logging.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
elki.distance.NumberVectorDistance<? super V>	getDistance()	Returns the distance.
elki.data.type.TypeInformation[]	getInputTypeRestriction()
Clustering<M>	run(elki.database.relation.Relation<V> relation)	Run the clustering algorithm.
void	setDistance(elki.distance.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 elki.logging.Logging LOG

The logger for this class.

trials

private int trials

Number of trials to do.

innerkMeans

private KMeans<V extends elki.data.NumberVector,M extends MeanModel> innerkMeans

Variant of kMeans for the bisecting step.

qualityMeasure

private KMeansQualityMeasure<? super V extends elki.data.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 elki.data.type.TypeInformation[] getInputTypeRestriction()

Specified by:

getInputTypeRestriction in interface elki.Algorithm

run

public Clustering<M> run(elki.database.relation.Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

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

Parameters:

relation - Relation to process.

Returns:

Clustering result

getDistance

public elki.distance.NumberVectorDistance<? super V> getDistance()

Description copied from interface: KMeans

Returns the distance.

Specified by:

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

Parameters:

k - K parameter

setDistance

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

Description copied from interface: KMeans

Set the distance function to use.

Specified by:

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

Parameters:

init - Initialization method