Uses of Interface
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

Packages that use KMeans

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

Uses of KMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that implement KMeans

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector,M extends Model> Abstract base class for k-means implementations.
class	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> Run K-Means multiple times, and keep the best run.
class	KMeansBatchedLloyd<V extends NumberVector> An algorithm for k-means, using Lloyd-style bulk iterations.
class	KMeansBisecting<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	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansHybridLloydMacQueen<V extends NumberVector> A hybrid k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
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 KMedoidsPAM instead).
class	SingleAssignmentKMeans<V extends NumberVector> Pseudo-k-Means variations, that assigns each object to the nearest center.
class	XMeans<V extends NumberVector,M extends MeanModel> X-means: Extending K-means with Efficient Estimation on the Number of Clusters.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans declared as KMeans

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type KMeans

Constructor and Description
BestOfMultipleKMeans(int trials, KMeans<V,M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure) Constructor.
KMeansBisecting(int k, KMeans<V,M> innerkMeans) Constructor.
XMeans(NumberVectorDistanceFunction<? super V> distanceFunction, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization<? super V> initializer, PredefinedInitialMeans splitInitializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random) Constructor.

Uses of KMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization declared as KMeans

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with parameters of type KMeans

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

Uses of KMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel that implement KMeans

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

Uses of KMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain declared as KMeans

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with parameters of type KMeans

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

Uses of KMeans in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering declared as KMeans

Modifier and Type	Field and Description
(package private) KMeans<O,?>	KMeansOutlierDetection.clusterer Clustering algorithm to use
(package private) KMeans<O,?>	KMeansOutlierDetection.Parameterizer.clusterer Clustering algorithm to use

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type KMeans

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

Uses of KMeans in tutorial.clustering

Classes in tutorial.clustering that implement KMeans

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