Uses of Interface
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization

Packages that use KMeansInitialization

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of KMeansInitialization in de.lmu.ifi.dbs.elki.algorithm.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering declared as KMeansInitialization

Modifier and Type	Field and Description
private KMeansInitialization<V>	EM.initializer Class to choose the initial means
protected KMeansInitialization<V>	EM.Parameterizer.initializer

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering with parameters of type KMeansInitialization

Constructor and Description
EM(int k, double delta, KMeansInitialization<V> initializer, int maxiter) Constructor.

Uses of KMeansInitialization in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that implement KMeansInitialization

Modifier and Type	Class and Description
class	AbstractKMeansInitialization<V> Abstract base class for common k-means initializations.
class	FirstKInitialMeans<V> Initialize K-means by using the first k objects as initial means.
class	KMeansPlusPlusInitialMeans<V,D extends NumberDistance<D,?>> K-Means++ initialization for k-means.
class	PAMInitialMeans<V,D extends NumberDistance<D,?>> PAM initialization for k-means (and of course, PAM).
class	RandomlyChosenInitialMeans<V> Initialize K-means by randomly choosing k exsiting elements as cluster centers.
class	RandomlyGeneratedInitialMeans<V extends NumberVector<?>> Initialize k-means by generating random vectors (within the data sets value range).

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans declared as KMeansInitialization

Modifier and Type	Field and Description
protected KMeansInitialization<V>	KMeansLloyd.Parameterizer.initializer Initialization method.
protected KMeansInitialization<V>	AbstractKMeans.initializer Method to choose initial means.
protected KMeansInitialization<V>	KMeansMacQueen.Parameterizer.initializer Initialization method.
protected KMeansInitialization<V>	KMediansLloyd.Parameterizer.initializer Initialization method.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type KMeansInitialization

Constructor and Description
AbstractKMeans(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMeansLloyd(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMeansMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMediansLloyd(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.

Uses of KMeansInitialization in tutorial.clustering

Fields in tutorial.clustering declared as KMeansInitialization

Modifier and Type	Field and Description
protected KMeansInitialization<V>	SameSizeKMeansAlgorithm.Parameterizer.initializer Initialization method.

Constructors in tutorial.clustering with parameters of type KMeansInitialization

Constructor and Description
SameSizeKMeansAlgorithm(PrimitiveDoubleDistanceFunction<? super NumberVector<?>> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.