Package jsat.clustering.kmeans

Class Summary

Class	Description
ElkanKernelKMeans	An efficient implementation of the K-Means algorithm.
ElkanKMeans	An efficient implementation of the K-Means algorithm.
GMeans	This class provides a method of performing KMeans clustering when the value of K is not known.
HamerlyKMeans	An efficient implementation of the K-Means algorithm.
KernelKMeans	Base class for various Kernel K Means implementations.
KMeans	Base class for the numerous implementations of k-means that exist.
KMeansPDN	This class provides a method of performing KMeans clustering when the value of K is not known.
LloydKernelKMeans	An implementation of the naive algorithm for performing kernel k-means.
MiniBatchKMeans	Implements the mini-batch algorithms for k-means.
NaiveKMeans	An implementation of Lloyd's K-Means clustering algorithm using the naive algorithm.
XMeans	This class provides a method of performing KMeans clustering when the value of K is not known.