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

Interface KMeansQualityMeasure<O extends NumberVector<?>,D extends Distance<?>>

Type Parameters:

O - Input Object restriction type

D - Distance restriction type

All Known Implementing Classes:

WithinClusterMeanDistanceQualityMeasure, WithinClusterVarianceQualityMeasure

public interface KMeansQualityMeasure<O extends NumberVector<?>,D extends Distance<?>>

Interface for computing the quality of a K-Means clustering.

Method Summary

Methods

Modifier and Type	Method and Description
<V extends O> double	calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends D> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.

Method Detail

calculateCost

<V extends O> double calculateCost(Clustering<? extends MeanModel<V>> clustering,
                                 PrimitiveDistanceFunction<? super V,? extends D> distanceFunction,
                                 Relation<V> relation)

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