Uses of Class
de.lmu.ifi.dbs.elki.data.model.OPTICSModel

Packages that use OPTICSModel

de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots

Uses of OPTICSModel in de.lmu.ifi.dbs.elki.algorithm.clustering

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

private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrderResult<N> clusterOrderResult, Relation<?> relation, double ixi, int minpts) Extract clusters from a cluster order result.
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)

Uses of OPTICSModel in de.lmu.ifi.dbs.elki.visualization.visualizers.optics

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.optics with type parameters of type OPTICSModel

(package private) Clustering<OPTICSModel>

OPTICSClusterVisualization.clus Our clustering

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.optics that return types with arguments of type OPTICSModel

protected static Clustering<OPTICSModel>

OPTICSClusterVisualization.findOPTICSClustering(Result result) Find the first OPTICS clustering child of a result.

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.optics with type arguments of type OPTICSModel

private void

OPTICSClusterVisualization.drawClusters(List<Cluster<OPTICSModel>> clusters, int depth) Recursively draw clusters