Package elki.clustering.kmeans.quality

Class BayesianInformationCriterionZhao

java.lang.Object

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

elki.clustering.kmeans.quality.BayesianInformationCriterionZhao

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>

@Reference(authors="Q. Zhao, M. Xu, P. Fr\u00e4nti",
           title="Knee Point Detection on Bayesian Information Criterion",
           booktitle="20th IEEE International Conference on Tools with Artificial Intelligence",
           url="https://doi.org/10.1109/ICTAI.2008.154",
           bibkey="DBLP:conf/ictai/ZhaoXF08")
public class BayesianInformationCriterionZhao
extends AbstractKMeansQualityMeasure<NumberVector>

Different version of the BIC criterion.

Reference:

Q. Zhao, M. Xu, P. Fränti
Knee Point Detection on Bayesian Information Criterion
20th IEEE International Conference on Tools with Artificial Intelligence

Since:

0.7.0

Author:

Tibor Goldschwendt, Erich Schubert

Constructor Summary

Constructors

Constructor	Description
BayesianInformationCriterionZhao()

Method Summary

Modifier and Type	Method	Description
boolean	isBetter(double currentCost, double bestCost)	Compare two scores.
static double	logLikelihoodZhao(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
<V extends NumberVector> double	quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, 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

BayesianInformationCriterionZhao

public BayesianInformationCriterionZhao()

Method Detail

quality

public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering,
                                               NumberVectorDistance<? super V> distance,
                                               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

logLikelihoodZhao

public static double logLikelihoodZhao(Relation<? extends NumberVector> relation,
                                       Clustering<? extends MeanModel> clustering,
                                       NumberVectorDistance<?> distance)

Computes log likelihood of an entire clustering.

Version as used by Zhao et al.

Parameters:

relation - Data relation

clustering - Clustering

distance - Distance function

Returns:

Log Likelihood.

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.