Uses of Class
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

Packages that use AbstractKMeans

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation

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

Subclasses of AbstractKMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Modifier and Type	Class and Description
class	KMeansAnnulus<V extends NumberVector> Annulus k-means algorithm.
class	KMeansCompare<V extends NumberVector> Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansExponion<V extends NumberVector> Newlings's exponion k-means algorithm, exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	KMeansMinusMinus<V extends NumberVector> k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMeansSimplifiedElkan<V extends NumberVector> Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	KMeansSort<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	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.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return AbstractKMeans

Modifier and Type	Method and Description
protected abstract AbstractKMeans<V,?>	AbstractKMeans.Parameterizer.makeInstance()

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

Subclasses of AbstractKMeans in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

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

Uses of AbstractKMeans in tutorial.clustering

Subclasses of AbstractKMeans in tutorial.clustering

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