de.jungblut.clustering

Class DBSCANClustering

java.lang.Object

de.jungblut.clustering.DBSCANClustering

public final class DBSCANClustering
extends Object

Plain sequential DBSCAN clustering.

Author:

thomas.jungblut

Constructor Summary

Constructors

Constructor and Description
DBSCANClustering()

Method Summary

Modifier and Type	Method and Description
static List<List<de.jungblut.math.DoubleVector>>	cluster(List<de.jungblut.math.DoubleVector> points, DistanceMeasurer measurer, int minPoints, double epsilon) Clusters the given points.
static List<de.jungblut.math.DoubleVector>	findNoise(List<de.jungblut.math.DoubleVector> points, List<List<de.jungblut.math.DoubleVector>> clusteringOutput) Find the noise in the given clustering, by taking a set difference.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DBSCANClustering

public DBSCANClustering()

Method Detail

cluster

public static List<List<de.jungblut.math.DoubleVector>> cluster(List<de.jungblut.math.DoubleVector> points,
                                                                DistanceMeasurer measurer,
                                                                int minPoints,
                                                                double epsilon)

Clusters the given points.

Parameters:

points - the points to cluster.

measurer - the distance measurer to use.

minPoints - the number of minimum points in a cluster.

epsilon - the radius of a point from which we find neighbours.

Returns:

the assignments of the points. Points that are not included are classified as noise.

findNoise

public static List<de.jungblut.math.DoubleVector> findNoise(List<de.jungblut.math.DoubleVector> points,
                                                            List<List<de.jungblut.math.DoubleVector>> clusteringOutput)

Find the noise in the given clustering, by taking a set difference.

Returns:

a list of points that were classified as noise.