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

Class BayesianInformationCriterionZhao

java.lang.Object

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

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

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>

@Reference(authors="Q. Zhao, M. Xu, P. Fra\u0308nti",
           title="Knee Point Detection on Bayesian Information Criterion",
           booktitle="20th IEEE International Conference on Tools with Artificial Intelligence",
           url="http://dx.doi.org/10.1109/ICTAI.2008.154")
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:

0.2

Author:

Tibor Goldschwendt, Erich Schubert

Constructor Summary

Constructors

Constructor and Description
BayesianInformationCriterionZhao()

Method Summary

Methods

Modifier and Type	Method and Description
boolean	ascending() Use ascending or descending ordering.
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, logLikelihoodAlternate, numberOfFreeParameters, numPoints, varianceOfCluster

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,
                                      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

ascending

public boolean ascending()

Description copied from interface: KMeansQualityMeasure

Use ascending or descending ordering.

Returns:

true when larger scores are better.

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.