Uses of Interface
jsat.clustering.KClusterer

Packages that use KClusterer

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

Uses of KClusterer in jsat.clustering

Classes in jsat.clustering that implement KClusterer

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	KClustererBase A base foundation that provides an implementation of the methods that return a list of lists for the clusterings using their int array counterparts.
class	PAM

Methods in jsat.clustering that return KClusterer

Modifier and Type	Method and Description
abstract KClusterer	KClustererBase.clone()
KClusterer	KClusterer.clone()

Constructors in jsat.clustering with parameters of type KClusterer

Constructor and Description
GapStatistic(KClusterer base) Creates a new Gap clusterer using the base clustering algorithm given.
GapStatistic(KClusterer base, boolean PCSampling) Creates a new Gap clsuterer using the base clustering algorithm given.
GapStatistic(KClusterer base, boolean PCSampling, int B, DistanceMetric dm) Creates a new Gap clsuterer using the base clustering algorithm given.

Uses of KClusterer in jsat.clustering.hierarchical

Classes in jsat.clustering.hierarchical that implement KClusterer

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).

Constructors in jsat.clustering.hierarchical with parameters of type KClusterer

Constructor and Description
DivisiveGlobalClusterer(KClusterer baseClusterer, ClusterEvaluation clusterEvaluation)
DivisiveLocalClusterer(KClusterer baseClusterer, ClusterEvaluation clusterEvaluation)

Uses of KClusterer in jsat.clustering.kmeans

Classes in jsat.clustering.kmeans that implement KClusterer

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.