de.lmu.ifi.dbs.elki.algorithm.clustering

Class DBSCAN<O>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,Clustering<Model>>

de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN<O>

Type Parameters:

O - the type of Object the algorithm is applied to

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<Model>>, DistanceBasedAlgorithm<O>

@Title(value="DBSCAN: Density-Based Clustering of Applications with Noise")
 @Description(value="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(value=200)
public class DBSCAN<O>
extends AbstractDistanceBasedAlgorithm<O,Clustering<Model>>
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 and Description
static class	DBSCAN.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
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.
protected long	ncounter Number of neighbors.
protected ModifiableDBIDs	noise Holds a set of noise.
protected ModifiableDBIDs	processedIDs Holds a set of processed ids.
protected java.util.List<ModifiableDBIDs>	resultList Holds a list of clusters found.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
DBSCAN(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor with parameters.

Method Summary

Modifier and Type	Method and Description
protected void	expandCluster(Relation<O> relation, RangeQuery<O> rangeQuery, DBIDRef startObjectID, ArrayModifiableDBIDs seeds, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private void	processNeighbors(DoubleDBIDListIter neighbor, ModifiableDBIDs currentCluster, ArrayModifiableDBIDs seeds) Process a single core point.
Clustering<Model>	run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.
protected void	runDBSCAN(Relation<O> relation, RangeQuery<O> rangeQuery) Run the DBSCAN algorithm

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

epsilon

protected double epsilon

Holds the epsilon radius threshold.

minpts

protected int minpts

Holds the minimum cluster size.

resultList

protected java.util.List<ModifiableDBIDs> resultList

Holds a list of clusters found.

noise

protected ModifiableDBIDs noise

Holds a set of noise.

processedIDs

protected ModifiableDBIDs processedIDs

Holds a set of processed ids.

ncounter

protected long ncounter

Number of neighbors.

Constructor Detail

DBSCAN

public DBSCAN(DistanceFunction<? super O> distanceFunction,
              double epsilon,
              int minpts)

Constructor with parameters.

Parameters:

distanceFunction - Distance function

epsilon - Epsilon value

minpts - Minpts parameter

Method Detail

run

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

Performs the DBSCAN algorithm on the given database.

runDBSCAN

protected void runDBSCAN(Relation<O> relation,
                         RangeQuery<O> rangeQuery)

Run the DBSCAN algorithm

Parameters:

relation - Data relation

rangeQuery - Range query class

expandCluster

protected void expandCluster(Relation<O> relation,
                             RangeQuery<O> rangeQuery,
                             DBIDRef startObjectID,
                             ArrayModifiableDBIDs seeds,
                             FiniteProgress objprog,
                             IndefiniteProgress clusprog)

DBSCAN-function expandCluster. Border-Objects become members of the first possible cluster.

Parameters:

relation - Database relation to run on

rangeQuery - Range query to use

startObjectID - potential seed of a new potential cluster

seeds - Array to store the current seeds

objprog - Number of objects processed (may be null)

clusprog - Number of clusters found (may be null)

processNeighbors

private void processNeighbors(DoubleDBIDListIter neighbor,
                              ModifiableDBIDs currentCluster,
                              ArrayModifiableDBIDs seeds)

Process a single core point.

Parameters:

neighbor - Iterator over neighbors

currentCluster - Current cluster

seeds - Seed set

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

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

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<Model>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<Model>>

Returns:

the static logger