Uses of Interface
jsat.clustering.Clusterer

Packages that use Clusterer

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

Uses of Clusterer in jsat.clustering

Subinterfaces of Clusterer in jsat.clustering

Modifier and Type	Interface and Description
interface	KClusterer Defines a clustering method that requires the number of clusters in the data set to be known before hand.

Classes in jsat.clustering that implement Clusterer

Modifier and Type	Class and Description
class	CLARA
class	ClustererBase A base foundation that provides an implementation of ClustererBase.cluster(jsat.DataSet) and ClustererBase.cluster(jsat.DataSet, java.util.concurrent.ExecutorService) using their int array counterparts.
class	DBSCAN A density-based algorithm for discovering clusters in large spatial databases with noise (1996) by Martin Ester , Hans-peter Kriegel , Jörg S , Xiaowei Xu
class	EMGaussianMixture An implementation of Gaussian Mixture models that learns the specified number of Gaussians using Expectation Maximization algorithm.
class	FLAME Provides an implementation of the FLAME clustering algorithm.
class	GapStatistic This class implements a method for estimating the number of clusters in a data set called the Gap Statistic.
class	HDBSCAN HDBSCAN is a density based clustering algorithm that is an improvement over DBSCAN.
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	LSDBC A parallel implementation of Locally Scaled Density Based Clustering.
class	MeanShift The MeanShift algorithms performs clustering on a data set by letting the data speak for itself and performing a mode search amongst the data set, returning a cluster for each discovered mode.
class	OPTICS An Implementation of the OPTICS algorithm, which is a generalization of DBSCAN.
class	PAM

Methods in jsat.clustering that return Clusterer

Modifier and Type	Method and Description
abstract Clusterer	ClustererBase.clone()
Clusterer	Clusterer.clone()

Uses of Clusterer in jsat.clustering.hierarchical

Classes in jsat.clustering.hierarchical that implement Clusterer

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 Clusterer in jsat.clustering.kmeans

Classes in jsat.clustering.kmeans that implement Clusterer

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.