@Title("Kmeans Initialization Strategies")
Package elki.clustering.kmeans.initialization
Initialization strategies for kmeans.

Interface Summary Interface Description KMeansInitialization Interface for initializing KMeans 
Class Summary Class Description AbstractKMeansInitialization Abstract base class for common kmeans initializations.AbstractKMeansInitialization.Par Parameterization class.AFKMC2 AFKMC² initializationAFKMC2.Instance Abstract instance implementing the weight handling.AFKMC2.Par Parameterization class.FarthestPoints<O> KMeans initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).FarthestPoints.Par<O> Parameterization class.FarthestSumPoints<O> KMeans initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).FarthestSumPoints.Par<V> Parameterization class.FirstK<O> Initialize Kmeans by using the first k objects as initial means.FirstK.Par<V extends NumberVector> Parameterization class.KMC2 KMC² initializationKMC2.Instance Abstract instance implementing the weight handling.KMC2.Par Parameterization class.KMeansPlusPlus<O> KMeans++ initialization for kmeans.KMeansPlusPlus.Instance<T> Abstract instance implementing the weight handling.KMeansPlusPlus.MedoidsInstance Instance for kmedoids.KMeansPlusPlus.NumberVectorInstance Instance for kmeans, number vector based.KMeansPlusPlus.Par<V> Parameterization class.Ostrovsky Ostrovsky initial means, a variant of kmeans++ that is expected to give slightly better results on average, but only works for kmeans and not for, e.g., PAM (kmedoids).Ostrovsky.Par Parameterization class.Predefined Run kmeans with prespecified initial means.Predefined.Par Parameterization class.RandomlyChosen<O> Initialize Kmeans by randomly choosing k existing elements as initial cluster centers.RandomlyChosen.Par<V> Parameterization class.RandomNormalGenerated Initialize kmeans by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).RandomNormalGenerated.Par Parameterization class.RandomUniformGenerated Initialize kmeans by generating random vectors (uniform, within the value range of the data set).RandomUniformGenerated.Par Parameterization class.SampleKMeans<V extends NumberVector> Initialize kmeans by running kmeans on a sample of the data set only.SphericalAFKMC2 Spherical KMeans++ initialization with markov chains.SphericalAFKMC2.Instance Abstract instance implementing the weight handling.SphericalAFKMC2.Par Parameterization class.SphericalKMeansPlusPlus<O> Spherical KMeans++ initialization for kmeans.SphericalKMeansPlusPlus.Instance Abstract instance implementing the weight handling.SphericalKMeansPlusPlus.Par<V> Parameterization class.