org.apache.commons.math3.ml.clustering

## Class DBSCANClusterer<T extends Clusterable>

- java.lang.Object
- - org.apache.commons.math3.ml.clustering.Clusterer<T>
  - - org.apache.commons.math3.ml.clustering.DBSCANClusterer<T>

- Type Parameters:
    `T` - type of the points to cluster

      public class DBSCANClusterer<T extends Clusterable>
      extends 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
  Version:
    $Id: DBSCANClusterer.java 1503290 2013-07-15 15:16:29Z sebb $
  See Also:
    DBSCAN (wikipedia), A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

- - ### Constructor Summary

    Constructors
    Constructor and Description
    `DBSCANClusterer(double eps, int minPts)` Creates a new instance of a DBSCANClusterer.
    `DBSCANClusterer(double eps, int minPts, DistanceMeasure measure)` Creates a new instance of a DBSCANClusterer.

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `List<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.

    - ### 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 Detail

    - #### DBSCANClusterer

          public DBSCANClusterer(double eps,
                         int minPts)
                          throws 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:
        `NotPositiveException` - if `eps < 0.0` or `minPts < 0`

    - #### DBSCANClusterer

          public DBSCANClusterer(double eps,
                         int minPts,
                         DistanceMeasure measure)
                          throws 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:
        `NotPositiveException` - if `eps < 0.0` or `minPts < 0`

  - ### Method Detail

    - #### 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<Cluster<T>> cluster(Collection<T> points)
                                                       throws NullArgumentException

      Performs DBSCAN cluster analysis.
      Specified by:
        `cluster` in class `Clusterer<T extends Clusterable>`
      Parameters:
        `points` - the points to cluster
      Returns:
        the list of clusters
      Throws:
        `NullArgumentException` - if the data points are null

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