@Title("Quality Measures for K-means")

Package elki.clustering.kmeans.quality

Quality measures for k-Means results.

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).
AkaikeInformationCriterionXMeans	Akaike Information Criterion (AIC).
BayesianInformationCriterion	Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.
BayesianInformationCriterionXMeans	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.
WithinClusterMeanDistance	Class for computing the average overall distance.
WithinClusterVariance	Class for computing the variance in a clustering result (sum-of-squares).