Uses of Class
elki.clustering.kmeans.AbstractKMeans

Packages that use AbstractKMeans

Package	Description
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.

Uses of AbstractKMeans in elki.clustering.kmeans

Subclasses of AbstractKMeans in elki.clustering.kmeans

Modifier and Type	Class	Description
class	AnnulusKMeans<V extends elki.data.NumberVector>	Annulus k-means algorithm.
class	BetulaLloydKMeans	BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	CompareMeans<V extends elki.data.NumberVector>	Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	ElkanKMeans<V extends elki.data.NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	ExponionKMeans<V extends elki.data.NumberVector>	Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
class	GMeans<V extends elki.data.NumberVector,M extends MeanModel>	G-Means extends K-Means and estimates the number of centers with Anderson Darling Test. Implemented as specialization of XMeans.
class	HamerlyKMeans<V extends elki.data.NumberVector>	Hamerly's fast k-means by exploiting the triangle inequality.
class	HartiganWongKMeans<V extends elki.data.NumberVector>	Hartigan and Wong k-means clustering.
class	KDTreeFilteringKMeans<V extends elki.data.NumberVector>	Filtering or "blacklisting" K-means with k-d-tree acceleration.
class	KDTreePruningKMeans<V extends elki.data.NumberVector>	Pruning K-means with k-d-tree acceleration.
class	KMeansMinusMinus<V extends elki.data.NumberVector>	k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMediansLloyd<V extends elki.data.NumberVector>	k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).
class	LloydKMeans<V extends elki.data.NumberVector>	The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class	MacQueenKMeans<V extends elki.data.NumberVector>	The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	ShallotKMeans<V extends elki.data.NumberVector>	Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
class	SimplifiedElkanKMeans<V extends elki.data.NumberVector>	Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	SingleAssignmentKMeans<V extends elki.data.NumberVector>	Pseudo-k-means variations, that assigns each object to the nearest center.
class	SortMeans<V extends elki.data.NumberVector>	Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class	XMeans<V extends elki.data.NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
class	YinYangKMeans<V extends elki.data.NumberVector>	Yin-Yang k-Means Clustering.

Methods in elki.clustering.kmeans that return AbstractKMeans

Modifier and Type	Method	Description
abstract AbstractKMeans<V,?>	AbstractKMeans.Par.make()

Uses of AbstractKMeans in elki.clustering.kmeans.parallel

Subclasses of AbstractKMeans in elki.clustering.kmeans.parallel

Modifier and Type	Class	Description
class	ParallelLloydKMeans<V extends elki.data.NumberVector>	Parallel implementation of k-Means clustering.

Uses of AbstractKMeans in elki.clustering.kmeans.spherical

Subclasses of AbstractKMeans in elki.clustering.kmeans.spherical

Modifier and Type	Class	Description
class	EuclideanSphericalElkanKMeans<V extends elki.data.NumberVector>	Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalHamerlyKMeans<V extends elki.data.NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	SphericalElkanKMeans<V extends elki.data.NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	SphericalHamerlyKMeans<V extends elki.data.NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalKMeans<V extends elki.data.NumberVector>	The standard spherical k-means algorithm.
class	SphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalSimplifiedHamerlyKMeans<V extends elki.data.NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalSingleAssignmentKMeans<V extends elki.data.NumberVector>	Pseudo-k-Means variations, that assigns each object to the nearest center.