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

Class WithinClusterVarianceQualityMeasure

java.lang.Object

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

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector<?>,NumberDistance<?,?>>

public class WithinClusterVarianceQualityMeasure
extends Object
implements KMeansQualityMeasure<NumberVector<?>,NumberDistance<?,?>>

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

Constructor Summary

Constructors

Constructor and Description
WithinClusterVarianceQualityMeasure()

Method Summary

Methods

Modifier and Type	Method and Description
<V extends NumberVector<?>> double	calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> 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

WithinClusterVarianceQualityMeasure

public WithinClusterVarianceQualityMeasure()

Method Detail

calculateCost

public <V extends NumberVector<?>> double calculateCost(Clustering<? extends MeanModel<V>> clustering,
                                               PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction,
                                               Relation<V> relation)

Description copied from interface: KMeansQualityMeasure

Calculates and returns the quality measure.

Specified by:

calculateCost in interface KMeansQualityMeasure<NumberVector<?>,NumberDistance<?,?>>

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