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.

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.