Package elki.clustering.kmeans.quality

Class AkaikeInformationCriterion

java.lang.Object

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

elki.clustering.kmeans.quality.AkaikeInformationCriterion

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>

@Reference(authors="H. Akaike",title="Information Theory and an Extension of the Maximum Likelihood Principle",booktitle="Second International Symposium on Information Theory",bibkey="conf/isit/Akaike73") @Reference(authors="D. Pelleg, A. Moore",title="X-means: Extending K-means with Efficient Estimation on the Number of Clusters",booktitle="Proc. 17th Int. Conf. on Machine Learning (ICML 2000)",url="http://www.pelleg.org/shared/hp/download/xmeans.ps",bibkey="DBLP:conf/icml/PellegM00")
public class AkaikeInformationCriterion
extends AbstractKMeansQualityMeasure<NumberVector>

Akaike Information Criterion (AIC).

Reference:

H. Akaike
Information Theory and an Extension of the Maximum Likelihood Principle
Second International Symposium on Information Theory

The use for k-means was briefly mentioned in:

D. Pelleg, A. Moore
X-means: Extending K-means with Efficient Estimation on the Number of Clusters
In: Proceedings of the 17th International Conference on Machine Learning (ICML 2000)

Since:

0.7.0

Author:

Tibor Goldschwendt, Erich Schubert

Constructor Summary

Constructors

Constructor	Description
AkaikeInformationCriterion()

Method Summary

Modifier and Type	Method	Description
boolean	isBetter(double currentCost, double bestCost)	Compare two scores.
<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

AkaikeInformationCriterion

public AkaikeInformationCriterion()

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

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.