## Uses of Classde.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.AbstractKMeansInitialization

• Packages that use AbstractKMeansInitialization
Package Description
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization
Initialization strategies for k-means.
• ### Uses of AbstractKMeansInitialization in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Modifier and Type Class and Description
class  FarthestPointsInitialMeans<O>
K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).
class  FarthestSumPointsInitialMeans<O>
K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).
class  KMeansPlusPlusInitialMeans<O>
K-Means++ initialization for k-means.
class  OstrovskyInitialMeans<O>
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  PredefinedInitialMeans
Run k-means with prespecified initial means.
class  RandomlyChosenInitialMeans<O>
Initialize K-means by randomly choosing k existing elements as initial cluster centers.
class  RandomNormalGeneratedInitialMeans
Initialize k-means by generating random vectors (normal distributed with $$N(\mu,\sigma)$$ in each dimension).
class  RandomUniformGeneratedInitialMeans
Initialize k-means by generating random vectors (uniform, within the value range of the data set).
class  SampleKMeansInitialization<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.
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