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

Packages that use OPTICSModel

Package	Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Field and Description
(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

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
private void	OPTICSClusterVisualization.drawClusters(List<Cluster<OPTICSModel>> clusters, int depth) Recursively draw clusters