Uses of Class
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.P3C.ClusterCandidate

Packages that use P3C.ClusterCandidate

Package	Description
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 P3C.ClusterCandidate in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return types with arguments of type P3C.ClusterCandidate

Modifier and Type	Method and Description
private ArrayList<P3C.ClusterCandidate>	P3C.hardClustering(WritableDataStore<double[]> probClusterIGivenX, List<P3C.Signature> clusterCores, DBIDs dbids) Creates a hard clustering from the specified soft membership matrix.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type P3C.ClusterCandidate

Modifier and Type	Method and Description
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.