Uses of Class
jsat.clustering.KClustererBase

Packages that use KClustererBase

Package	Description
jsat.clustering
jsat.clustering.hierarchical
jsat.clustering.kmeans

Uses of KClustererBase in jsat.clustering

Subclasses of KClustererBase in jsat.clustering

Modifier and Type	Class and Description
class	CLARA
class	EMGaussianMixture An implementation of Gaussian Mixture models that learns the specified number of Gaussians using Expectation Maximization algorithm.
class	GapStatistic This class implements a method for estimating the number of clusters in a data set called the Gap Statistic.
class	PAM

Uses of KClustererBase in jsat.clustering.hierarchical

Subclasses of KClustererBase in jsat.clustering.hierarchical

Modifier and Type	Class and Description
class	DivisiveGlobalClusterer DivisiveGlobalClusterer is a hierarchical clustering method that works by splitting the data set into sub trees from the top down.
class	DivisiveLocalClusterer DivisiveLocalClusterer is a hierarchical clustering method that works by splitting the data set into sub trees from the top down.
class	NNChainHAC This class implements Hierarchical Agglomerative Clustering via the Nearest Neighbor Chain approach.
class	PriorityHAC
class	SimpleHAC Provides a naive implementation of hierarchical agglomerative clustering (HAC).

Uses of KClustererBase in jsat.clustering.kmeans

Subclasses of KClustererBase in jsat.clustering.kmeans

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