Package org.aksw.jena_sparql_api.sparql.ext.geosparql

Class DBSCANClusterer<T extends org.apache.commons.math3.ml.clustering.Clusterable>

java.lang.Object
org.apache.commons.math3.ml.clustering.Clusterer<T>
org.aksw.jena_sparql_api.sparql.ext.geosparql.DBSCANClusterer<T>
Type Parameters:
T - type of the points to cluster
public class DBSCANClusterer<T extends org.apache.commons.math3.ml.clustering.Clusterable> extends org.apache.commons.math3.ml.clustering.Clusterer<T>
DBSCAN (density-based spatial clustering of applications with noise) algorithm.

The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e. a point p is density connected to another point q, if there exists a chain of points pi, with i = 1 .. n and p1 = p and pn = q, such that each pair <pi, pi+1> is directly density-reachable. A point q is directly density-reachable from point p if it is in the ε-neighborhood of this point.

Any point that is not density-reachable from a formed cluster is treated as noise, and will thus not be present in the result.

The algorithm requires two parameters:

  • eps: the distance that defines the ε-neighborhood of a point
  • minPoints: the minimum number of density-connected points required to form a cluster
Since:
3.2
See Also:

  • Constructor Summary

    Constructors
    Constructor
    Description
    DBSCANClusterer(double eps, int minPts)
    Creates a new instance of a DBSCANClusterer.
    DBSCANClusterer(double eps, int minPts, org.apache.commons.math3.ml.distance.DistanceMeasure measure)
    Creates a new instance of a DBSCANClusterer.

  • Method Summary

    Modifier and Type
    Method
    Description
    List<org.apache.commons.math3.ml.clustering.Cluster<T>>
    cluster(Collection<T> points)
    Performs DBSCAN cluster analysis.
    double
    getEps()
    Returns the maximum radius of the neighborhood to be considered.
    int
    getMinPts()
    Returns the minimum number of points needed for a cluster.
    protected List<T>
    getNeighbors(T point, Collection<T> points)
    Returns a list of density-reachable neighbors of a point.

    Methods inherited from class org.apache.commons.math3.ml.clustering.Clusterer

    distance, getDistanceMeasure

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • DBSCANClusterer

      public DBSCANClusterer(double eps, int minPts) throws org.apache.commons.math3.exception.NotPositiveException
      Creates a new instance of a DBSCANClusterer.

      The euclidean distance will be used as default distance measure.

      Parameters:
      eps - maximum radius of the neighborhood to be considered
      minPts - minimum number of points needed for a cluster
      Throws:
      org.apache.commons.math3.exception.NotPositiveException - if eps < 0.0 or minPts < 0

    • DBSCANClusterer

      public DBSCANClusterer(double eps, int minPts, org.apache.commons.math3.ml.distance.DistanceMeasure measure) throws org.apache.commons.math3.exception.NotPositiveException
      Creates a new instance of a DBSCANClusterer.
      Parameters:
      eps - maximum radius of the neighborhood to be considered
      minPts - minimum number of points needed for a cluster
      measure - the distance measure to use
      Throws:
      org.apache.commons.math3.exception.NotPositiveException - if eps < 0.0 or minPts < 0

  • Method Details

    • getEps

      public double getEps()
      Returns the maximum radius of the neighborhood to be considered.
      Returns:
      maximum radius of the neighborhood

    • getMinPts

      public int getMinPts()
      Returns the minimum number of points needed for a cluster.
      Returns:
      minimum number of points needed for a cluster

    • cluster

      public List<org.apache.commons.math3.ml.clustering.Cluster<T>> cluster(Collection<T> points) throws org.apache.commons.math3.exception.NullArgumentException
      Performs DBSCAN cluster analysis.
      Specified by:
      cluster in class org.apache.commons.math3.ml.clustering.Clusterer<T extends org.apache.commons.math3.ml.clustering.Clusterable>
      Parameters:
      points - the points to cluster
      Returns:
      the list of clusters
      Throws:
      org.apache.commons.math3.exception.NullArgumentException - if the data points are null

    • getNeighbors

      protected List<T> getNeighbors(T point, Collection<T> points)
      Returns a list of density-reachable neighbors of a point.
      Parameters:
      point - the point to look for
      points - possible neighbors
      Returns:
      the List of neighbors