Package org.apache.commons.math4.legacy.ml.clustering

Class DBSCANClusterer<T extends Clusterable>

java.lang.Object

org.apache.commons.math4.legacy.ml.clustering.Clusterer<T>

org.apache.commons.math4.legacy.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 p_i, with i = 1 .. n and p₁ = p and p_n = q, such that each pair <p_i, p_i+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:

DBSCAN (wikipedia), A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

Constructor Summary

Constructors

Constructor	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

Modifier and Type	Method	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.math4.legacy.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)

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)

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)

Performs DBSCAN cluster analysis.

Specified by:

cluster in class Clusterer<T extends Clusterable>

Parameters:

points - Points to cluster (cannot be null).

Returns:

the list of clusters.