Package elki.clustering.kmeans.quality

Class WithinClusterVariance

java.lang.Object

elki.clustering.kmeans.quality.AbstractKMeansQualityMeasure<elki.data.NumberVector>

elki.clustering.kmeans.quality.WithinClusterVariance

All Implemented Interfaces:

KMeansQualityMeasure<elki.data.NumberVector>

public class WithinClusterVariance
extends AbstractKMeansQualityMeasure<elki.data.NumberVector>

Class for computing the variance in a clustering result (sum-of-squares).

Since:

0.6.0

Author:

Stephan Baier, Erich Schubert

Constructor Summary

Constructors

Constructor	Description
WithinClusterVariance()

Method Summary

Modifier and Type	Method	Description
boolean	isBetter(double currentCost, double bestCost)	Compare two scores.
<V extends elki.data.NumberVector> double	quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)	Calculates and returns the quality measure.

Methods inherited from class elki.clustering.kmeans.quality.AbstractKMeansQualityMeasure

logLikelihood, numberOfFreeParameters, numPoints, varianceContributionOfCluster

Methods inherited from class java.lang.Object

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

Constructor Detail

WithinClusterVariance

public WithinClusterVariance()

Method Detail

quality

public <V extends elki.data.NumberVector> double quality(Clustering<? extends MeanModel> clustering,
                                                         elki.distance.NumberVectorDistance<? super V> distance,
                                                         elki.database.relation.Relation<V> relation)

Description copied from interface: KMeansQualityMeasure

Calculates and returns the quality measure.

Type Parameters:

V - Actual vector type (could be a subtype of O!)

Parameters:

clustering - Clustering to analyze

distance - Distance function to use (usually Euclidean or squared Euclidean!)

relation - Relation for accessing objects

Returns:

quality measure

isBetter

public boolean isBetter(double currentCost,
                        double bestCost)

Description copied from interface: KMeansQualityMeasure

Compare two scores.

Parameters:

currentCost - New (candiate) cost/score

bestCost - Existing best cost/score (may be NaN)

Returns:

true when the new score is better, or the old score is NaN.