Uses of Class
elki.clustering.kmeans.initialization.AbstractKMeansInitialization

Packages that use AbstractKMeansInitialization

Package	Description
elki.clustering.kmeans.initialization	Initialization strategies for k-means.

Uses of AbstractKMeansInitialization in elki.clustering.kmeans.initialization

Subclasses of AbstractKMeansInitialization in elki.clustering.kmeans.initialization

Modifier and Type	Class	Description
class	AFKMC2	AFK-MC² initialization
class	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	KMC2	K-MC² initialization
class	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.