@Title("K-means Initialization Strategies")

Package elki.clustering.kmeans.initialization

Initialization strategies for k-means.

Interface Summary

Interface	Description
KMeansInitialization	Interface for initializing K-Means

Class Summary

Class	Description
AbstractKMeansInitialization	Abstract base class for common k-means initializations.
AbstractKMeansInitialization.Par	Parameterization class.
AFKMC2	AFK-MC² initialization
AFKMC2.Instance	Abstract instance implementing the weight handling.
AFKMC2.Par	Parameterization class.
FarthestPoints<O>	K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).
FarthestPoints.Par<O>	Parameterization class.
FarthestSumPoints<O>	K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).
FarthestSumPoints.Par<V>	Parameterization class.
FirstK<O>	Initialize K-means by using the first k objects as initial means.
FirstK.Par<V extends NumberVector>	Parameterization class.
KMC2	K-MC² initialization
KMC2.Instance	Abstract instance implementing the weight handling.
KMC2.Par	Parameterization class.
KMeansPlusPlus<O>	K-Means++ initialization for k-means.
KMeansPlusPlus.Instance<T>	Abstract instance implementing the weight handling.
KMeansPlusPlus.MedoidsInstance	Instance for k-medoids.
KMeansPlusPlus.NumberVectorInstance	Instance for k-means, number vector based.
KMeansPlusPlus.Par<V>	Parameterization 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).
Ostrovsky.Par	Parameterization class.
Predefined	Run k-means with prespecified initial means.
Predefined.Par	Parameterization class.
RandomlyChosen<O>	Initialize K-means by randomly choosing k existing elements as initial cluster centers.
RandomlyChosen.Par<V>	Parameterization class.
RandomNormalGenerated	Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
RandomNormalGenerated.Par	Parameterization class.
RandomUniformGenerated	Initialize k-means by generating random vectors (uniform, within the value range of the data set).
RandomUniformGenerated.Par	Parameterization class.
SampleKMeans<V extends NumberVector>	Initialize k-means by running k-means on a sample of the data set only.
SphericalAFKMC2	Spherical K-Means++ initialization with markov chains.
SphericalAFKMC2.Instance	Abstract instance implementing the weight handling.
SphericalAFKMC2.Par	Parameterization class.
SphericalKMeansPlusPlus<O>	Spherical K-Means++ initialization for k-means.
SphericalKMeansPlusPlus.Instance	Abstract instance implementing the weight handling.
SphericalKMeansPlusPlus.Par<V>	Parameterization class.