org.hipparchus.clustering

Class DBSCANClusterer<T extends Clusterable>

java.lang.Object

org.hipparchus.clustering.Clusterer<T>

org.hipparchus.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

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

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.hipparchus.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 MathIllegalArgumentException

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:

MathIllegalArgumentException - if eps < 0.0 or minPts < 0

DBSCANClusterer

public DBSCANClusterer(double eps,
                       int minPts,
                       DistanceMeasure measure)
                throws MathIllegalArgumentException

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:

MathIllegalArgumentException - 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