Package elki.clustering.dbscan

Class DBSCAN<O>

java.lang.Object

elki.clustering.dbscan.DBSCAN<O>

Type Parameters:

O - the type of Object the algorithm is applied to

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<Model>>

@Title("DBSCAN: Density-Based Clustering of Applications with Noise")
@Description("Algorithm to find density-connected sets in a database based on the parameters \'minpts\' and \'epsilon\' (specifying a volume). These two parameters determine a density threshold for clustering.")
@Reference(authors="Martin Ester, Hans-Peter Kriegel, J\u00f6rg Sander, Xiaowei Xu",title="A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise",booktitle="Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD \'96)",url="http://www.aaai.org/Library/KDD/1996/kdd96-037.php",bibkey="DBLP:conf/kdd/EsterKSX96") @Reference(authors="Erich Schubert, J\u00f6rg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu",title="DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN",booktitle="ACM Trans. Database Systems (TODS)",url="https://doi.org/10.1145/3068335",bibkey="DBLP:journals/tods/SchubertSEKX17")
@Priority(200)
public class DBSCAN<O>
extends java.lang.Object
implements ClusteringAlgorithm<Clustering<Model>>

Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.

Reference:

Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD '96)

Further discussion:

Erich Schubert, Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
ACM Trans. Database Systems (TODS)

Since:

0.1

Author:

Arthur Zimek, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
private class	DBSCAN.Instance	Instance for a single data set.
static class	DBSCAN.Par<O>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
protected Distance<? super O>	distance	Distance function used.
protected double	epsilon	Holds the epsilon radius threshold.
private static Logging	LOG	The logger for this class.
protected int	minpts	Holds the minimum cluster size.

Constructor Summary

Constructors

Constructor	Description
DBSCAN(Distance<? super O> distance, double epsilon, int minpts)	Constructor with parameters.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<Model>	run(Relation<O> relation)	Performs the DBSCAN algorithm on the given database.

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

The logger for this class.

distance

protected Distance<? super O> distance

Distance function used.

epsilon

protected double epsilon

Holds the epsilon radius threshold.

minpts

protected int minpts

Holds the minimum cluster size.

Constructor Detail

DBSCAN

public DBSCAN(Distance<? super O> distance,
              double epsilon,
              int minpts)

Constructor with parameters.

Parameters:

distance - Distance function

epsilon - Epsilon value

minpts - Minpts parameter

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

run

public Clustering<Model> run(Relation<O> relation)

Performs the DBSCAN algorithm on the given database.