Uses of Interface
elki.clustering.kmeans.initialization.KMeansInitialization
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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 elki.clustering.uncertain Clustering algorithms for uncertain data.tutorial.clustering Classes from the tutorial on implementing a custom k-means variation. -
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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 meansprotected KMeansInitialization
DiagonalGaussianModelFactory.Par. initializer
Initialization methodprotected KMeansInitialization
MultivariateGaussianModelFactory. initializer
Class to choose the initial meansprotected KMeansInitialization
MultivariateGaussianModelFactory.Par. initializer
Initialization methodprotected KMeansInitialization
SphericalGaussianModelFactory. initializer
Class to choose the initial meansprotected KMeansInitialization
SphericalGaussianModelFactory.Par. initializer
Initialization methodprotected KMeansInitialization
TextbookMultivariateGaussianModelFactory. initializer
Class to choose the initial meansprotected KMeansInitialization
TextbookMultivariateGaussianModelFactory.Par. initializer
Initialization methodprotected KMeansInitialization
TextbookSphericalGaussianModelFactory. initializer
Class to choose the initial meansprotected KMeansInitialization
TextbookSphericalGaussianModelFactory.Par. initializer
Initialization methodprotected KMeansInitialization
TwoPassMultivariateGaussianModelFactory. initializer
Class to choose the initial meansprotected KMeansInitialization
TwoPassMultivariateGaussianModelFactory.Par. initializer
Initialization methodConstructors 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 centersMethods 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(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.AnnulusKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)
Constructor.CompareMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.ElkanKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)
Constructor.ExponionKMeans(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(NumberVectorDistance<? super V> distance, double critical, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, RandomFactory random)
Constructor.HamerlyKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)
Constructor.HartiganWongKMeans(int k, KMeansInitialization initializer)
Constructor.KDTreeFilteringKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)
Constructor.KDTreePruningKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)
Constructor.KMeansMinusMinus(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, double rate, boolean noiseFlag)
Constructor.KMediansLloyd(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.LloydKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.MacQueenKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.ShallotKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)
Constructor.SimplifiedElkanKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)
Constructor.SingleAssignmentKMeans(NumberVectorDistance<? super V> distance, int k, KMeansInitialization initializer)
Constructor.SortMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
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.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 initializationclass
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 initializationclass
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 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(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. -
Uses of KMeansInitialization in elki.clustering.uncertain
Constructors in elki.clustering.uncertain with parameters of type KMeansInitialization Constructor Description CKMeans(NumberVectorDistance<? super NumberVector> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor that uses Lloyd's k-means algorithm. -
Uses of KMeansInitialization in tutorial.clustering
Fields in tutorial.clustering declared as KMeansInitialization Modifier and Type Field Description protected KMeansInitialization
SameSizeKMeans.Par. initializer
Initialization method.Constructors in tutorial.clustering with parameters of type KMeansInitialization Constructor Description SameSizeKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.
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