Class OPTICS<T>

java.lang.Object
kieker.analysis.generic.clustering.optics.OPTICS<T>
Type Parameters:
T - model type

public class OPTICS<T>
extends java.lang.Object
An implementation of the OPTICS algorithm. A detailed explanation of the algorithm can be found in the paper "OPTICS: ordering points to identify the clustering structure".
Since:
2.0.0
Author:
Lars Jürgensen
  • Constructor Summary

    Constructors 
    Constructor Description
    OPTICS​(MTree<OpticsData<T>> mtree, double maxDistance, int minPTs, java.util.List<OpticsData<T>> models)  
  • Method Summary

    Modifier and Type Method Description
    java.util.List<OpticsData<T>> calculate()
    This calculates the OPTICS result.
    double getMaxDistance()  
    int getMinPTs()  
    MTree<OpticsData<T>> getMtree()  

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
  • Constructor Details

    • OPTICS

      public OPTICS​(MTree<OpticsData<T>> mtree, double maxDistance, int minPTs, java.util.List<OpticsData<T>> models)
      Parameters:
      mtree - The M-Tree with the behavior models to be clustered
      maxDistance - The maximal distance two neighbors can have (the epsilon value of the algorithm)
      minPTs - The minimal amount of neighbors a object must have to be called a core-object
      models - A list of all models to be clustered. They have to be the same as the models in the M-Tree
  • Method Details

    • calculate

      public java.util.List<OpticsData<T>> calculate()
      This calculates the OPTICS result.
      Returns:
      An ordered list of the behavior models. The reachability distances of the models are important for the evaluation.
    • getMinPTs

      public int getMinPTs()
    • getMaxDistance

      public double getMaxDistance()
    • getMtree

      public MTree<OpticsData<T>> getMtree()