Uses of Interface
elki.clustering.kmeans.initialization.KMeansInitialization

Packages that use KMeansInitialization

Package	Description
elki.clustering.em.models
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.kmedoids.initialization

Uses of KMeansInitialization in elki.clustering.em.models

Fields in elki.clustering.em.models declared as KMeansInitialization

Modifier and Type	Field	Description
protected KMeansInitialization	DiagonalGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	DiagonalGaussianModelFactory.Par.initializer	Initialization method
protected KMeansInitialization	MultivariateGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	MultivariateGaussianModelFactory.Par.initializer	Initialization method
protected KMeansInitialization	SphericalGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	SphericalGaussianModelFactory.Par.initializer	Initialization method
protected KMeansInitialization	TextbookMultivariateGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	TextbookMultivariateGaussianModelFactory.Par.initializer	Initialization method
protected KMeansInitialization	TextbookSphericalGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	TextbookSphericalGaussianModelFactory.Par.initializer	Initialization method
protected KMeansInitialization	TwoPassMultivariateGaussianModelFactory.initializer	Class to choose the initial means
protected KMeansInitialization	TwoPassMultivariateGaussianModelFactory.Par.initializer	Initialization method

Constructors in elki.clustering.em.models with parameters of type KMeansInitialization

Constructor	Description
DiagonalGaussianModelFactory(KMeansInitialization initializer)	Constructor.
MultivariateGaussianModelFactory(KMeansInitialization initializer)	Constructor.
SphericalGaussianModelFactory(KMeansInitialization initializer)	Constructor.
TextbookMultivariateGaussianModelFactory(KMeansInitialization initializer)	Constructor.
TextbookSphericalGaussianModelFactory(KMeansInitialization initializer)	Constructor.
TwoPassMultivariateGaussianModelFactory(KMeansInitialization initializer)	Constructor.

Uses of KMeansInitialization in elki.clustering.kmeans

Fields in elki.clustering.kmeans declared as KMeansInitialization

Modifier and Type	Field	Description
protected KMeansInitialization	AbstractKMeans.initializer	Method to choose initial means.
protected KMeansInitialization	AbstractKMeans.Par.initializer	Initialization method.
(package private) KMeansInitialization	FuzzyCMeans.initializer	Produces initial cluster.
(package private) KMeansInitialization	FuzzyCMeans.Par.initializer	K-Means init for initial cluster centers

Methods in elki.clustering.kmeans with parameters of type KMeansInitialization

Modifier and Type	Method	Description
void	AbstractKMeans.setInitializer(KMeansInitialization init)
void	BestOfMultipleKMeans.setInitializer(KMeansInitialization init)
void	BisectingKMeans.setInitializer(KMeansInitialization init)
void	KMeans.setInitializer(KMeansInitialization init)	Set the initialization method.

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

Constructor	Description
AbstractKMeans(int k, int maxiter, KMeansInitialization initializer)	Constructor.
AbstractKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
AnnulusKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
CompareMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
ElkanKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
ExponionKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
FuzzyCMeans(int k, int miniter, int maxiter, double delta, double m, boolean soft, KMeansInitialization initialization)	Constructor.
GMeans(elki.distance.NumberVectorDistance<? super V> distance, double critical, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, elki.utilities.random.RandomFactory random)	Constructor.
HamerlyKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
HartiganWongKMeans(int k, KMeansInitialization initializer)	Constructor.
KDTreeFilteringKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)	Constructor.
KDTreePruningKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)	Constructor.
KMeansMinusMinus(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, double rate, boolean noiseFlag)	Constructor.
KMediansLloyd(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
LloydKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
MacQueenKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
ShallotKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SimplifiedElkanKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SingleAssignmentKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, KMeansInitialization initializer)	Constructor.
SortMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
XMeans(elki.distance.NumberVectorDistance<? super V> distance, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, KMeansQualityMeasure<V> informationCriterion, elki.utilities.random.RandomFactory random)	Constructor.
YinYangKMeans(int k, int maxiter, KMeansInitialization initializer, int t)	Constructor.

Uses of KMeansInitialization in elki.clustering.kmeans.initialization

Classes in elki.clustering.kmeans.initialization that implement KMeansInitialization

Modifier and Type	Class	Description
class	AbstractKMeansInitialization	Abstract base class for common k-means initializations.
class	AFKMC2	AFK-MC² initialization
class	FarthestPoints<O>	K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).
class	FarthestSumPoints<O>	K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).
class	FirstK<O>	Initialize K-means by using the first k objects as initial means.
class	KMC2	K-MC² initialization
class	KMeansPlusPlus<O>	K-Means++ initialization for k-means.
class	Ostrovsky	Ostrovsky initial means, a variant of k-means++ that is expected to give slightly better results on average, but only works for k-means and not for, e.g., PAM (k-medoids).
class	Predefined	Run k-means with prespecified initial means.
class	RandomlyChosen<O>	Initialize K-means by randomly choosing k existing elements as initial cluster centers.
class	RandomNormalGenerated	Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
class	RandomUniformGenerated	Initialize k-means by generating random vectors (uniform, within the value range of the data set).
class	SampleKMeans<V extends elki.data.NumberVector>	Initialize k-means by running k-means on a sample of the data set only.
class	SphericalAFKMC2	Spherical K-Means++ initialization with markov chains.
class	SphericalKMeansPlusPlus<O>	Spherical K-Means++ initialization for k-means.

Uses of KMeansInitialization in elki.clustering.kmeans.parallel

Constructors in elki.clustering.kmeans.parallel with parameters of type KMeansInitialization

Constructor	Description
ParallelLloydKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.

Uses of KMeansInitialization in elki.clustering.kmeans.spherical

Constructors in elki.clustering.kmeans.spherical with parameters of type KMeansInitialization

Constructor	Description
EuclideanSphericalElkanKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
EuclideanSphericalHamerlyKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
EuclideanSphericalSimplifiedElkanKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SphericalElkanKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SphericalHamerlyKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SphericalKMeans(int k, int maxiter, KMeansInitialization initializer)	Constructor.
SphericalSimplifiedElkanKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SphericalSimplifiedHamerlyKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SphericalSingleAssignmentKMeans(int k, KMeansInitialization initializer)	Constructor.

Uses of KMeansInitialization in elki.clustering.kmedoids.initialization

Classes in elki.clustering.kmedoids.initialization that implement KMeansInitialization

Modifier and Type	Class	Description
class	BUILD<O>	PAM initialization for k-means (and of course, for PAM).
class	LAB<O>	Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
class	ParkJun<O>	Initialization method proposed by Park and Jun.