Uses of Interface
elki.clustering.kmeans.KMeans

Packages that use KMeans

Package	Description
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.outlier.clustering	Clustering based outlier detection.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of KMeans in elki.clustering.kmeans

Classes in elki.clustering.kmeans that implement KMeans

Modifier and Type	Class	Description
class	AbstractKMeans<V extends NumberVector,M extends Model>	Abstract base class for k-means implementations.
class	AnnulusKMeans<V extends NumberVector>	Annulus k-means algorithm.
class	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>	Run K-Means multiple times, and keep the best run.
class	BetulaLloydKMeans	BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	BisectingKMeans<V extends NumberVector,M extends MeanModel>	The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
class	CompareMeans<V extends NumberVector>	Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	ElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	ExponionKMeans<V extends NumberVector>	Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
class	GMeans<V extends 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 NumberVector>	Hamerly's fast k-means by exploiting the triangle inequality.
class	HartiganWongKMeans<V extends NumberVector>	Hartigan and Wong k-means clustering.
class	KDTreeFilteringKMeans<V extends NumberVector>	Filtering or "blacklisting" K-means with k-d-tree acceleration.
class	KDTreePruningKMeans<V extends NumberVector>	Pruning K-means with k-d-tree acceleration.
class	KMeansMinusMinus<V extends NumberVector>	k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMediansLloyd<V extends 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 NumberVector>	The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class	MacQueenKMeans<V extends NumberVector>	The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	ShallotKMeans<V extends NumberVector>	Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
class	SimplifiedElkanKMeans<V extends NumberVector>	Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	SingleAssignmentKMeans<V extends NumberVector>	Pseudo-k-means variations, that assigns each object to the nearest center.
class	SortMeans<V extends 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 NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
class	YinYangKMeans<V extends NumberVector>	Yin-Yang k-Means Clustering.

Fields in elki.clustering.kmeans declared as KMeans

Modifier and Type	Field	Description
private KMeans<V,M>	BestOfMultipleKMeans.innerkMeans	Variant of kMeans for the bisecting step.
private KMeans<V,M>	BisectingKMeans.innerkMeans	Variant of kMeans for the bisecting step.
protected KMeans<V,M>	XMeans.innerKMeans	Inner k-means algorithm.
protected KMeans<V,M>	XMeans.Par.innerKMeans	Variant of kMeans
protected KMeans<V,M>	BestOfMultipleKMeans.Par.kMeansVariant	Variant of kMeans to use.
protected KMeans<V,M>	BisectingKMeans.Par.kMeansVariant	Variant of kMeans

Constructors in elki.clustering.kmeans with parameters of type KMeans

Constructor	Description
BestOfMultipleKMeans(int trials, KMeans<V,M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure)	Constructor.
BisectingKMeans(int k, KMeans<V,M> innerkMeans)	Constructor.
GMeans(NumberVectorDistance<? super V> distance, double critical, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, RandomFactory random)	Constructor.
XMeans(NumberVectorDistance<? super V> distance, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random)	Constructor.

Uses of KMeans in elki.clustering.kmeans.initialization

Fields in elki.clustering.kmeans.initialization declared as KMeans

Modifier and Type	Field	Description
private KMeans<V,?>	SampleKMeans.innerkMeans	Variant of kMeans to use for initialization.
protected KMeans<V,?>	SampleKMeans.Par.innerkMeans	Inner k-means algorithm to use.

Constructors in elki.clustering.kmeans.initialization with parameters of type KMeans

Constructor	Description
SampleKMeans(RandomFactory rnd, KMeans<V,?> innerkMeans, double rate)	Constructor.

Uses of KMeans in elki.clustering.kmeans.parallel

Classes in elki.clustering.kmeans.parallel that implement KMeans

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

Uses of KMeans in elki.clustering.kmeans.spherical

Classes in elki.clustering.kmeans.spherical that implement KMeans

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

Uses of KMeans in elki.clustering.uncertain

Fields in elki.clustering.uncertain declared as KMeans

Modifier and Type	Field	Description
private KMeans<?,KMeansModel>	CKMeans.Par.kmeans	K-means instance to use.

Constructors in elki.clustering.uncertain with parameters of type KMeans

Constructor	Description
CKMeans(KMeans<?,KMeansModel> kmeans)	Constructor that uses an arbitrary k-means algorithm.

Uses of KMeans in elki.outlier.clustering

Fields in elki.outlier.clustering declared as KMeans

Modifier and Type	Field	Description
(package private) KMeans<O,?>	KMeansOutlierDetection.clusterer	K-Means clustering algorithm to use
(package private) KMeans<O,?>	KMeansOutlierDetection.Par.clusterer	Clustering algorithm to use

Constructors in elki.outlier.clustering with parameters of type KMeans

Constructor	Description
KMeansOutlierDetection(KMeans<O,?> clusterer, KMeansOutlierDetection.Rule rule)	Constructor.

Uses of KMeans in tutorial.clustering

Classes in tutorial.clustering that implement KMeans

Modifier and Type	Class	Description
class	SameSizeKMeans<V extends NumberVector>	K-means variation that produces equally sized clusters.