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

Class BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans<V,M>

Type Parameters:

V - Vector type

M - Model type

All Implemented Interfaces:

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

public class BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
extends AbstractAlgorithm<Clustering<M>>
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

Modifier and Type	Class and Description
static class	BestOfMultipleKMeans.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and 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 class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
DistanceFunction<? super V>	getDistanceFunction() Returns the distanceFunction.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<M>	run(Database database, Relation<V> relation) Run the clustering algorithm.
void	setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) 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 de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

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

run

public Clustering<M> run(Database database,
                         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:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

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

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<M extends MeanModel>>

Returns:

Type restriction

getDistanceFunction

public DistanceFunction<? super V> getDistanceFunction()

Description copied from interface: DistanceBasedAlgorithm

Returns the distanceFunction.

Specified by:

getDistanceFunction in interface DistanceBasedAlgorithm<V extends NumberVector>

Returns:

the distanceFunction

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

setDistanceFunction

public void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)

Description copied from interface: KMeans

Set the distance function to use.

Specified by:

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

Parameters:

distanceFunction - 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

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<M extends MeanModel>>

Returns:

the static logger