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.
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<?>,D extends Distance<D>,M extends MeanModel<V>> Abstract base class for k-means implementations.
class	BestOfMultipleKMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Run K-Means multiple times, and keep the best run.
class	KMeansBatchedLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansBisecting<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> 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	KMeansHybridLloydMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
class	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.
class	KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.

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

Modifier and Type	Field and Description
private KMeans<V,D,M>	KMeansBisecting.innerkMeans Variant of kMeans for the bisecting step.
private KMeans<V,D,?>	SampleKMeansInitialization.innerkMeans Variant of kMeans for the bisecting step.
protected KMeans<V,D,?>	SampleKMeansInitialization.Parameterizer.innerkMeans Inner k-means algorithm to use.
private KMeans<V,D,M>	BestOfMultipleKMeans.innerkMeans Variant of kMeans for the bisecting step.
protected KMeans<V,D,M>	KMeansBisecting.Parameterizer.kMeansVariant Variant of kMeans
protected KMeans<V,D,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,D,M> innerkMeans, KMeansQualityMeasure<? super V,? super D> qualityMeasure) Constructor.
KMeansBisecting(int k, KMeans<V,D,M> innerkMeans) Constructor.
SampleKMeansInitialization(RandomFactory rnd, KMeans<V,D,?> innerkMeans, double rate) 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.