## Uses of Classelki.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

Modifier and Type Class Description
class  AFKMC2
AFK-MC² initialization
class
K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).
class
K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).
class  KMC2
K-MC² initialization
class
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
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
Spherical K-Means++ initialization for k-means.