Package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Initialization strategies for k-means.

See: Description

Interface Summary

Interface	Description
KMeansInitialization<V extends NumberVector>	Interface for initializing K-Means
KMedoidsInitialization<V>	Interface for initializing K-Medoids.

Class Summary

Class	Description
AbstractKMeansInitialization<V extends NumberVector>	Abstract base class for common k-means initializations.
AbstractKMeansInitialization.Parameterizer	Parameterization class.
FarthestPointsInitialMeans<O>	K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).
FarthestPointsInitialMeans.Parameterizer<O>	Parameterization class.
FarthestSumPointsInitialMeans<O>	K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).
FarthestSumPointsInitialMeans.Parameterizer<V>	Parameterization class.
FirstKInitialMeans<O>	Initialize K-means by using the first k objects as initial means.
FirstKInitialMeans.Parameterizer<V extends NumberVector>	Parameterization class.
KMeansPlusPlusInitialMeans<O>	K-Means++ initialization for k-means.
KMeansPlusPlusInitialMeans.Parameterizer<V>	Parameterization class.
PAMInitialMeans<O>	PAM initialization for k-means (and of course, PAM).
PAMInitialMeans.Parameterizer<V>	Parameterization class.
PredefinedInitialMeans	Run k-means with prespecified initial means.
PredefinedInitialMeans.Parameterizer	Parameterization class.
RandomlyChosenInitialMeans<O>	Initialize K-means by randomly choosing k existing elements as cluster centers.
RandomlyChosenInitialMeans.Parameterizer<V>	Parameterization class.
RandomlyGeneratedInitialMeans	Initialize k-means by generating random vectors (within the data sets value range).
RandomlyGeneratedInitialMeans.Parameterizer	Parameterization class.
SampleKMeansInitialization<V extends NumberVector>	Initialize k-means by running k-means on a sample of the data set only.
SampleKMeansInitialization.Parameterizer<V extends NumberVector>	Parameterization class.

Package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization Description

Initialization strategies for k-means.