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

Packages that use de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.algorithm.clustering.em

Class and Description
KMeansInitialization Interface for initializing K-Means

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class and Description
KMeansInitialization Interface for initializing K-Means
KMedoidsInitialization Interface for initializing K-Medoids.
PredefinedInitialMeans Run k-means with prespecified initial means.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Class and Description
KMeansInitialization Interface for initializing K-Means

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Class and Description
KMeansInitialization Interface for initializing K-Means

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization used by de.lmu.ifi.dbs.elki.index.idistance

Class and Description
KMedoidsInitialization Interface for initializing K-Medoids.