Package org.apache.commons.math3.stat.clustering

Class DBSCANClusterer<T extends Clusterable<T>>

java.lang.Object
org.apache.commons.math3.stat.clustering.DBSCANClusterer<T>
Type Parameters:
T - type of the points to cluster
@Deprecated public class DBSCANClusterer<T extends Clusterable<T>> extends Object
Deprecated.
As of 3.2 (to be removed in 4.0), use DBSCANClusterer instead
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

Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting Cluster objects will have no defined center, i.e. Cluster.getCenter() will return null.

Since:
3.1
See Also:

  • Constructor Summary

    Constructors
    Constructor
    Description
    DBSCANClusterer(double eps, int minPts)
    Deprecated.
    Creates a new instance of a DBSCANClusterer.

  • Method Summary

    Modifier and Type
    Method
    Description
    List<Cluster<T>>
    cluster(Collection<T> points)
    Deprecated.
    Performs DBSCAN cluster analysis.
    double
    getEps()
    Deprecated.
    Returns the maximum radius of the neighborhood to be considered.
    int
    getMinPts()
    Deprecated.
    Returns the minimum number of points needed for a cluster.

    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 NotPositiveException
      Deprecated.
      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
      Throws:
      NotPositiveException - if eps < 0.0 or minPts < 0

  • Method Details

    • getEps

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

    • getMinPts

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

    • cluster

      public List<Cluster<T>> cluster(Collection<T> points) throws NullArgumentException
      Deprecated.
      Performs DBSCAN cluster analysis.

      Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting Cluster objects will have no defined center, i.e. Cluster.getCenter() will return null.

      Parameters:
      points - the points to cluster
      Returns:
      the list of clusters
      Throws:
      NullArgumentException - if the data points are null