@Title("Kmeans clustering")
Package elki.clustering.kmeans
Kmeans clustering and variations.

Interface Summary Interface Description KMeans<V extends NumberVector,M extends Model> Some constants and options shared among kmeans family algorithms. 
Class Summary Class Description AbstractKMeans<V extends NumberVector,M extends Model> Abstract base class for kmeans implementations.AbstractKMeans.Instance Inner instance for a run, for better encapsulation, that encapsulates the standard flow of most (but not all) kmeans variations.AbstractKMeans.Par<V extends NumberVector> Parameterization class.AnnulusKMeans<V extends NumberVector> Annulus kmeans algorithm.AnnulusKMeans.Instance Inner instance, storing state for a single data set.AnnulusKMeans.Par<V extends NumberVector> Parameterization class.BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> Run KMeans multiple times, and keep the best run.BetulaLloydKMeans BIRCH/BETULAbased clustering algorithm that simply treats the leafs of the CFTree as clusters.BetulaLloydKMeans.Par Parameterization class.BisectingKMeans<V extends NumberVector,M extends MeanModel> The bisecting kmeans algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.CompareMeans<V extends NumberVector> CompareMeans: Accelerated kmeans by exploiting the triangle inequality and pairwise distances of means to prune candidate means.CompareMeans.Instance Inner instance, storing state for a single data set.CompareMeans.Par<V extends NumberVector> Parameterization class.ElkanKMeans<V extends NumberVector> Elkan's fast kmeans by exploiting the triangle inequality.ElkanKMeans.Instance Inner instance, storing state for a single data set.ElkanKMeans.Par<V extends NumberVector> Parameterization class.ExponionKMeans<V extends NumberVector> Newlings's Exponion kmeans algorithm, exploiting the triangle inequality.ExponionKMeans.Instance Inner instance, storing state for a single data set.ExponionKMeans.Par<V extends NumberVector> Parameterization class.FuzzyCMeans<V extends NumberVector> Fuzzy Clustering developed by Dunn and revisited by BezdekFuzzyCMeans.Par Parameterization class.GMeans<V extends NumberVector,M extends MeanModel> GMeans extends KMeans and estimates the number of centers with Anderson Darling Test.
Implemented as specialization of XMeans.GMeans.Par<V extends NumberVector,M extends MeanModel> Parameterization class.HamerlyKMeans<V extends NumberVector> Hamerly's fast kmeans by exploiting the triangle inequality.HamerlyKMeans.Instance Inner instance, storing state for a single data set.HamerlyKMeans.Par<V extends NumberVector> Parameterization class.HartiganWongKMeans<V extends NumberVector> Hartigan and Wong kmeans clustering.HartiganWongKMeans.Instance Instance for a particular data set.HartiganWongKMeans.Parameterizer<V extends NumberVector> Parameterization class.KDTreeFilteringKMeans<V extends NumberVector> Filtering or "blacklisting" Kmeans with kdtree acceleration.KDTreeFilteringKMeans.Par<V extends NumberVector> Parameterization class.KDTreePruningKMeans<V extends NumberVector> Pruning Kmeans with kdtree acceleration.KDTreePruningKMeans.KDNode Node of the kdtree used internally.KDTreePruningKMeans.Par<V extends NumberVector> Parameterization class.KMeansMinusMinus<V extends NumberVector> kmeans: A Unified Approach to Clustering and Outlier Detection.KMeansMinusMinus.Par<V extends NumberVector> Parameterization class.KMediansLloyd<V extends NumberVector> kmedians clustering algorithm, but using Lloydstyle bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (seePAM
instead).KMediansLloyd.Instance Inner instance, storing state for a single data set.KMediansLloyd.Par<V extends NumberVector> Parameterization class.LloydKMeans<V extends NumberVector> The standard kmeans algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).LloydKMeans.Instance Inner instance, storing state for a single data set.LloydKMeans.Par<V extends NumberVector> Parameterization class.MacQueenKMeans<V extends NumberVector> The original kmeans algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.MacQueenKMeans.Instance Inner instance, storing state for a single data set.MacQueenKMeans.Par<V extends NumberVector> Parameterization class.ShallotKMeans<V extends NumberVector> Borgelt's Shallot kmeans algorithm, exploiting the triangle inequality.ShallotKMeans.Instance Inner instance, storing state for a single data set.ShallotKMeans.Par<V extends NumberVector> Parameterization class.SimplifiedElkanKMeans<V extends NumberVector> Simplified version of Elkan's kmeans by exploiting the triangle inequality.SimplifiedElkanKMeans.Instance Inner instance, storing state for a single data set.SimplifiedElkanKMeans.Par<V extends NumberVector> Parameterization class.SingleAssignmentKMeans<V extends NumberVector> Pseudokmeans variations, that assigns each object to the nearest center.SingleAssignmentKMeans.Instance Inner instance, storing state for a single data set.SingleAssignmentKMeans.Par<V extends NumberVector> Parameterization class.SortMeans<V extends NumberVector> SortMeans: Accelerated kmeans by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).SortMeans.Instance Inner instance, storing state for a single data set.SortMeans.Par<V extends NumberVector> Parameterization class.XMeans<V extends NumberVector,M extends MeanModel> Xmeans: Extending Kmeans with Efficient Estimation on the Number of Clusters.YinYangKMeans<V extends NumberVector> YinYang kMeans Clustering.YinYangKMeans.Instance Instance for a particular data set.YinYangKMeans.Par<V extends NumberVector> Parameterization class. 
Enum Summary Enum Description KDTreePruningKMeans.Split Splitting strategies for constructing the kdtree.