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

Class WithinClusterMeanDistanceQualityMeasure

java.lang.Object

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

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>

public class WithinClusterMeanDistanceQualityMeasure
extends java.lang.Object
implements KMeansQualityMeasure<NumberVector>

Class for computing the average overall distance.

The average of all average pairwise distances in a cluster.

Since:

0.6.0

Author:

Stephan Baier

Constructor Summary

Constructors

Constructor and Description
WithinClusterMeanDistanceQualityMeasure()

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 java.lang.Object

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

Constructor Detail

WithinClusterMeanDistanceQualityMeasure

public WithinClusterMeanDistanceQualityMeasure()

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.

Specified by:

quality in interface KMeansQualityMeasure<NumberVector>

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.

Specified by:

isBetter in interface KMeansQualityMeasure<NumberVector>

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.