Uses of Package
elki.clustering.kmeans.initialization.betula

Packages that use elki.clustering.kmeans.initialization.betula

Package	Description
elki.clustering.em.models
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization.betula	Initialization methods for BIRCH-based k-means and EM clustering.

Classes in elki.clustering.kmeans.initialization.betula used by elki.clustering.em.models

Class	Description
AbstractCFKMeansInitialization	Abstract base class for CF k-means initializations.

Classes in elki.clustering.kmeans.initialization.betula used by elki.clustering.kmeans

Class	Description
AbstractCFKMeansInitialization	Abstract base class for CF k-means initializations.

Classes in elki.clustering.kmeans.initialization.betula used by elki.clustering.kmeans.initialization.betula

Class	Description
AbstractCFKMeansInitialization	Abstract base class for CF k-means initializations.
AbstractCFKMeansInitialization.Par	Parameterization class.
CFInitWeight	Initialization weight function for k-means initialization with BETULA.
CFKPlusPlusLeaves	K-Means++-like initialization for BETULA k-means, treating the leaf clustering features as a flat list, and called "leaves" in the publication.
CFKPlusPlusLeaves.Par	Parameterization class.
CFKPlusPlusTree	Initialize K-means by following tree paths weighted by their variance contribution.
CFKPlusPlusTrunk	Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style initialization.
CFRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
CFWeightedRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.