Package elki.clustering.kmeans.initialization.betula

Initialization methods for BIRCH-based k-means and EM clustering.

Interface Summary

Interface	Description
CFInitWeight	Initialization weight function for k-means initialization with BETULA.

Class Summary

Class	Description
AbstractCFKMeansInitialization	Abstract base class for CF k-means initializations.
AbstractCFKMeansInitialization.Par	Parameterization class.
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.
CFKPlusPlusTree.Par	Parameterization class.
CFKPlusPlusTrunk	Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style initialization.
CFKPlusPlusTrunk.Par	Parameterization class.
CFRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
CFRandomlyChosen.Par	Parameterization class.
CFWeightedRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
CFWeightedRandomlyChosen.Par	Parameterization class.
InterclusterWeight	Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea of k-means++ initialization.
SquaredEuclideanWeight	Use the squared Euclidean distance only for distance measurement.
VarianceWeight	Variance-based weighting scheme for k-means clustering with BETULA.