Uses of Interface
elki.clustering.kmeans.quality.KMeansQualityMeasure

Packages that use KMeansQualityMeasure

Package	Description
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.quality	Quality measures for k-Means results.

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).