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

Class DBSCAN<O,D extends Distance<D>>

java.lang.Object

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

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

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

Type Parameters:

O - the type of Object the algorithm is applied to

D - the type of Distance used

All Implemented Interfaces:

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

@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="M. Ester, H.-P. Kriegel, J. Sander, and X. 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), Portland, OR, 1996",
           url="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1980")
public class DBSCAN<O,D extends Distance<D>>
extends AbstractDistanceBasedAlgorithm<O,D,Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>>

DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.

Reference:
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD '96), Portland, OR, 1996.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DBSCAN.Parameterizer<O,D extends Distance<D>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected D	epsilon Holds the epsilon radius threshold.
private static Logging	LOG The logger for this class.
protected int	minpts Holds the minimum cluster size.
protected ModifiableDBIDs	noise Holds a set of noise.
protected ModifiableDBIDs	processedIDs Holds a set of processed ids.
protected List<ModifiableDBIDs>	resultList Holds a list of clusters found.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
protected void	expandCluster(Relation<O> relation, RangeQuery<O,D> rangeQuery, DBIDRef startObjectID, 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.
Clustering<Model>	run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.

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

getDistanceFunction

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

makeParameterDistanceFunction, 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 D extends Distance<D> epsilon

Holds the epsilon radius threshold.

minpts

protected int minpts

Holds the minimum cluster size.

resultList

protected 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.

Constructor Detail

DBSCAN

public DBSCAN(DistanceFunction<? super O,D> distanceFunction,
      D 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.

expandCluster

protected void expandCluster(Relation<O> relation,
                 RangeQuery<O,D> rangeQuery,
                 DBIDRef startObjectID,
                 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

objprog - the progress object for logging the current status

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