Package elki.clustering.kmeans.initialization.betula
Initialization methods for BIRCH-based k-means and EM clustering.
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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.