Package de.lmu.ifi.dbs.elki.algorithm.clustering.em

Expectation-Maximization clustering algorithm.

See: Description

Interface Summary

Interface	Description
EMClusterModel<M extends MeanModel>	Models useable in EM clustering.
EMClusterModelFactory<V extends NumberVector,M extends MeanModel>	Factory for initializing the EM models.

Class Summary

Class	Description
AbstractEMModelFactory<V extends NumberVector,M extends MeanModel>	Abstract base class for initializing EM.
AbstractEMModelFactory.Parameterizer<V extends NumberVector>	Parameterization class.
DiagonalGaussianModel	Simpler model for a single Gaussian cluster, without covariances.
DiagonalGaussianModelFactory<V extends NumberVector>	Factory for EM with multivariate gaussian models using diagonal matrixes.
DiagonalGaussianModelFactory.Parameterizer<V extends NumberVector>	Parameterization class
EM<V extends NumberVector,M extends MeanModel>	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM).
EM.Parameterizer<V extends NumberVector,M extends MeanModel>	Parameterization class.
MultivariateGaussianModel	Model for a single Gaussian cluster.
MultivariateGaussianModelFactory<V extends NumberVector>	Factory for EM with multivariate gaussian models (with covariance; also known as Gaussian Mixture Modeling, GMM).
MultivariateGaussianModelFactory.Parameterizer<V extends NumberVector>	Parameterization class
SphericalGaussianModel	Simple spherical Gaussian cluster.
SphericalGaussianModelFactory<V extends NumberVector>	Factory for EM with multivariate gaussian models using diagonal matrixes.
SphericalGaussianModelFactory.Parameterizer<V extends NumberVector>	Parameterization class

Package de.lmu.ifi.dbs.elki.algorithm.clustering.em Description

Expectation-Maximization clustering algorithm.