Uses of Class
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS.PROCLUSCluster

Packages that use PROCLUS.PROCLUSCluster

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms.

Uses of PROCLUS.PROCLUSCluster 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 PROCLUS.PROCLUSCluster

Modifier and Type	Method and Description
private ArrayList<PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(ArrayDBIDs m_current, long[][] dimensions, Relation<V> database) Assigns the objects to the clusters.
private List<PROCLUS.PROCLUSCluster>	PROCLUS.finalAssignment(List<Pair<Vector,long[]>> dimensions, Relation<V> database) Refinement step to assign the objects to the final clusters.

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

Modifier and Type	Method and Description
private DBIDs	PROCLUS.computeBadMedoids(ArrayDBIDs m_current, ArrayList<PROCLUS.PROCLUSCluster> clusters, int threshold) Computes the bad medoids, where the medoid of a cluster with less than the specified threshold of objects is bad.
private double	PROCLUS.evaluateClusters(ArrayList<PROCLUS.PROCLUSCluster> clusters, long[][] dimensions, Relation<V> database) Evaluates the quality of the clusters.
private List<Pair<Vector,long[]>>	PROCLUS.findDimensions(ArrayList<PROCLUS.PROCLUSCluster> clusters, Relation<V> database) Refinement step that determines the set of correlated dimensions for each cluster centroid.