org.apache.commons.math3.stat.clustering

## Class DBSCANClusterer<T extends Clusterable<T>>

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

Deprecated.
As of 3.2 (to be removed in 4.0), use DBSCANClusterer instead

@Deprecated
public class DBSCANClusterer<T extends Clusterable<T>>
extends Object
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
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)
Deprecated.
Creates a new instance of a DBSCANClusterer.
• ### Method Summary

Methods
Modifier and Type Method and 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 Detail

• #### 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 Detail

• #### getEps

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