@Title("Quality Measures for K-means")
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). AkaikeInformationCriterionAkaike Information Criterion (AIC). AkaikeInformationCriterionXMeansAkaike Information Criterion (AIC). BayesianInformationCriterionBayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results. BayesianInformationCriterionXMeansBayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results. BayesianInformationCriterionZhaoDifferent version of the BIC criterion. WithinClusterMeanDistanceClass for computing the average overall distance. WithinClusterVarianceClass for computing the variance in a clustering result (sum-of-squares).