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

Quality measures for k-Means results.

See: Description

Interface Summary

Interface	Description
KMeansQualityMeasure<O extends NumberVector>	Interface for computing the quality of a K-Means clustering.

Class Summary

Class	Description
AbstractKMeansQualityMeasure<O extends NumberVector>	Base class for evaluating clusterings by information criteria (such as AIC or BIC).
AkaikeInformationCriterion	Akaike Information Criterion (AIC).
BayesianInformationCriterion	Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.
BayesianInformationCriterionZhao	Different version of the BIC criterion.
WithinClusterMeanDistanceQualityMeasure	Class for computing the average overall distance.
WithinClusterVarianceQualityMeasure	Class for computing the variance in a clustering result (sum-of-squares).

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

Quality measures for k-Means results.