Uses of Interface
elki.clustering.kmeans.quality.KMeansQualityMeasure
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Packages that use KMeansQualityMeasure Package Description elki.clustering.kmeans K-means clustering and variations.elki.clustering.kmeans.quality Quality measures for k-Means results. -
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Uses of KMeansQualityMeasure in elki.clustering.kmeans
Fields in elki.clustering.kmeans declared as KMeansQualityMeasure Modifier and Type Field Description (package private) KMeansQualityMeasure<V>
XMeans. informationCriterion
Information criterion to choose the better split.protected KMeansQualityMeasure<V>
XMeans.Par. informationCriterion
Information criterion.protected KMeansQualityMeasure<? super V>
BestOfMultipleKMeans.Par. qualityMeasure
Quality measure.private KMeansQualityMeasure<? super V>
BestOfMultipleKMeans. qualityMeasure
Quality measure which should be used.Constructors in elki.clustering.kmeans with parameters of type KMeansQualityMeasure Constructor Description BestOfMultipleKMeans(int trials, KMeans<V,M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure)
Constructor.XMeans(NumberVectorDistance<? super V> distance, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random)
Constructor. -
Uses of KMeansQualityMeasure in elki.clustering.kmeans.quality
Classes in elki.clustering.kmeans.quality that implement KMeansQualityMeasure Modifier and Type Class Description class
AbstractKMeansQualityMeasure<O extends NumberVector>
Base class for evaluating clusterings by information criteria (such as AIC or BIC).class
AkaikeInformationCriterion
Akaike Information Criterion (AIC).class
AkaikeInformationCriterionXMeans
Akaike Information Criterion (AIC).class
BayesianInformationCriterion
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.class
BayesianInformationCriterionXMeans
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.class
BayesianInformationCriterionZhao
Different version of the BIC criterion.class
WithinClusterMeanDistance
Class for computing the average overall distance.class
WithinClusterVariance
Class for computing the variance in a clustering result (sum-of-squares).
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