Uses of Package
elki.clustering.kmeans.initialization

Packages that use elki.clustering.kmeans.initialization

Package	Description
elki.clustering.em.models
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.kmedoids.initialization
elki.clustering.uncertain	Clustering algorithms for uncertain data.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Classes in elki.clustering.kmeans.initialization used by elki.clustering.em.models

Class	Description
KMeansInitialization	Interface for initializing K-Means

Classes in elki.clustering.kmeans.initialization used by elki.clustering.kmeans

Class	Description
KMeansInitialization	Interface for initializing K-Means
Predefined	Run k-means with prespecified initial means.

Classes in elki.clustering.kmeans.initialization used by elki.clustering.kmeans.initialization

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

Classes in elki.clustering.kmeans.initialization used by elki.clustering.kmeans.parallel

Class	Description
KMeansInitialization	Interface for initializing K-Means

Classes in elki.clustering.kmeans.initialization used by elki.clustering.kmeans.spherical

Class	Description
KMeansInitialization	Interface for initializing K-Means

Classes in elki.clustering.kmeans.initialization used by elki.clustering.kmedoids.initialization

Class	Description
AbstractKMeansInitialization.Par	Parameterization class.
KMeansInitialization	Interface for initializing K-Means

Classes in elki.clustering.kmeans.initialization used by elki.clustering.uncertain

Class	Description
KMeansInitialization	Interface for initializing K-Means

Classes in elki.clustering.kmeans.initialization used by tutorial.clustering

Class	Description
KMeansInitialization	Interface for initializing K-Means