Uses of Class
de.lmu.ifi.dbs.elki.algorithm.clustering.em.MultivariateGaussianModel

Packages that use MultivariateGaussianModel

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.

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

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

Modifier and Type	Method and Description
List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)

Uses of MultivariateGaussianModel in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type MultivariateGaussianModel

Modifier and Type	Method and Description
private void	P3C.assignUnassigned(Relation<V> relation, WritableDataStore<double[]> probClusterIGivenX, List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned) Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private void	P3C.computeFuzzyMembership(Relation<V> relation, ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, List<MultivariateGaussianModel> models, int dim) Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
private void	P3C.findOutliers(Relation<V> relation, List<MultivariateGaussianModel> models, ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise) Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.