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

Class GriDBSCAN<V extends NumberVector>

java.lang.Object

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

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

de.lmu.ifi.dbs.elki.algorithm.clustering.GriDBSCAN<V>

Type Parameters:

V - the type of vector the algorithm is applied to

All Implemented Interfaces:

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

@Reference(authors="S. Mahran and K. Mahar",
           title="Using grid for accelerating density-based clustering",
           booktitle="8th IEEE Int. Conf. on Computer and Information Technology",
           url="http://dx.doi.org/10.1109/CIT.2008.4594646")
public class GriDBSCAN<V extends NumberVector>
extends AbstractDistanceBasedAlgorithm<V,Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>>

Using Grid for Accelerating Density-Based Clustering. An accelerated DBSCAN version for numerical data and Lp-norms only, by partitioning the data set into overlapping grid cells. For best efficiency, the overlap of the grid cells must be chosen well. The authors suggest a grid width of 10 times epsilon. Because of partitioning the data, this version does not make use of indexes. Reference:

S. Mahran and K. Mahar:
Using grid for accelerating density-based clustering.
In 8th IEEE Int. Conf. on Computer and Information Technology, 2008.

Since:

0.7.1

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static interface	GriDBSCAN.Assignment Point assignment.
protected static class	GriDBSCAN.Border Border point assignment.
protected static class	GriDBSCAN.Core Core point assignment.
protected static class	GriDBSCAN.Instance<V extends NumberVector> Instance, for a single run.
protected static class	GriDBSCAN.MultiBorder Multiple border point assignment.
static class	GriDBSCAN.Parameterizer<O extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected double	epsilon Holds the epsilon radius threshold.
protected double	gridwidth Width of the grid cells.
private static Logging	LOG The logger for this class.
protected int	minpts Holds the minimum cluster size.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
GriDBSCAN(DistanceFunction<? super V> distanceFunction, double epsilon, int minpts, double gridwidth) Constructor with parameters.

Method Summary

Methods

Modifier and Type	Method and Description
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<V> 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 double epsilon

Holds the epsilon radius threshold.

minpts

protected int minpts

Holds the minimum cluster size.

gridwidth

protected double gridwidth

Width of the grid cells. Must be at least 2 epsilon!

Constructor Detail

GriDBSCAN

public GriDBSCAN(DistanceFunction<? super V> distanceFunction,
         double epsilon,
         int minpts,
         double gridwidth)

Constructor with parameters.

Parameters:

distanceFunction - Distance function

epsilon - Epsilon value

minpts - Minpts parameter

gridwidth - Grid width

Method Detail

run

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

Performs the DBSCAN algorithm on the given database.

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