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.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation

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.
LABInitialMeans Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
OstrovskyInitialMeans 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).
PAMInitialMeans PAM initialization for k-means (and of course, for PAM).
ParkInitialMeans Initialization method proposed by Park and Jun.
PredefinedInitialMeans Run k-means with prespecified initial means.
RandomlyChosenInitialMeans Initialize K-means by randomly choosing k existing elements as initial cluster centers.
RandomNormalGeneratedInitialMeans Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
RandomUniformGeneratedInitialMeans Initialize k-means by generating random vectors (uniform, within the value range of the data set).
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.

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

Class and Description
KMeansInitialization Interface for initializing K-Means