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

Class WithinClusterVarianceQualityMeasure

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AbstractKMeansQualityMeasure<NumberVector>

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

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>

public class WithinClusterVarianceQualityMeasure
extends AbstractKMeansQualityMeasure<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 and Description
WithinClusterVarianceQualityMeasure()

Method Summary

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

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AbstractKMeansQualityMeasure

logLikelihood, numberOfFreeParameters, numPoints, varianceOfCluster

Methods inherited from class java.lang.Object

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

Constructor Detail

WithinClusterVarianceQualityMeasure

public WithinClusterVarianceQualityMeasure()

Method Detail

quality

public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering,
                                               NumberVectorDistanceFunction<? super V> distanceFunction,
                                               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

distanceFunction - 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.