Uses of Interface
elki.clustering.kmeans.KMeans
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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. -
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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 (seePAM
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 kMeansprotected KMeans<V,M>
BestOfMultipleKMeans.Par. kMeansVariant
Variant of kMeans to use.protected KMeans<V,M>
BisectingKMeans.Par. kMeansVariant
Variant of kMeansConstructors 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 useConstructors 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.
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