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 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 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.