Uses of Interface
de.lmu.ifi.dbs.elki.algorithm.clustering.em.EMClusterModel

Packages that use EMClusterModel

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.

Uses of EMClusterModel in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.em that implement EMClusterModel

Modifier and Type	Class and Description
class	DiagonalGaussianModel Simpler model for a single Gaussian cluster, without covariances.
class	MultivariateGaussianModel Model for a single Gaussian cluster.
class	SphericalGaussianModel Simple spherical Gaussian cluster.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.em that return types with arguments of type EMClusterModel

Modifier and Type	Method and Description
List<? extends EMClusterModel<M>>	EMClusterModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df) Build the initial models

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.em with type arguments of type EMClusterModel

Modifier and Type	Method and Description
static double	EM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
static void	EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, List<? extends EMClusterModel<?>> models) Recompute the covariance matrixes.