GriDBSCAN (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.clustering.dbscan.GriDBSCAN<V>

Type Parameters:

V - the type of vector the algorithm is applied to

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<Model>>
```
@Title("GriDBSCAN: Using Grid for Accelerating Density-Based Clustering")
@Reference(authors="S. Mahran, K. Mahar",
           title="Using grid for accelerating density-based clustering",
           booktitle="8th IEEE Int. Conf. on Computer and Information Technology",
           url="https://doi.org/10.1109/CIT.2008.4594646",
           bibkey="DBLP:conf/IEEEcit/MahranM08")
public class GriDBSCAN<V extends elki.data.NumberVector>
extends java.lang.Object
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, 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 Description

protected static class GriDBSCAN.Instance<V extends elki.data.NumberVector>
Instance, for a single run.
- Nested classes/interfaces inherited from interface elki.Algorithm
  elki.Algorithm.Utils

Field Summary

Fields
Modifier and Type	Field	Description
`protected elki.distance.Distance<? super V>`	`distance`	Distance function used.
`protected double`	`epsilon`	Holds the epsilon radius threshold.
`protected double`	`gridwidth`	Width of the grid cells.
`private static elki.logging.Logging`	`LOG`	The logger for this class.
`protected int`	`minpts`	Holds the minimum cluster size.

Constructor Summary

Constructors
Constructor	Description
`GriDBSCAN(elki.distance.Distance<? super V> distance, double epsilon, int minpts, double gridwidth)`	Constructor with parameters.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`elki.data.type.TypeInformation[]`	`getInputTypeRestriction()`
`Clustering<Model>`	`run(elki.database.relation.Relation<V> 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 elki.logging.Logging LOG
```
    The logger for this class.
  - distance
```
protected elki.distance.Distance<? super V extends elki.data.NumberVector> distance
```
    Distance function used.
  - 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(elki.distance.Distance<? super V> distance,
                 double epsilon,
                 int minpts,
                 double gridwidth)
```
    Constructor with parameters.
    
    Parameters:
    
    distance - Distance function
    
    epsilon - Epsilon value
    
    minpts - Minpts parameter
    
    gridwidth - Grid width
- Method Detail
  - getInputTypeRestriction
```
public elki.data.type.TypeInformation[] getInputTypeRestriction()
```
    Specified by:
    
    getInputTypeRestriction in interface elki.Algorithm
  - run
```
public Clustering<Model> run(elki.database.relation.Relation<V> relation)
```
    Performs the DBSCAN algorithm on the given database.