weka.clusterers

Class DBSCAN

java.lang.Object

weka.clusterers.AbstractClusterer

weka.clusterers.DBSCAN

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.clusterers.Clusterer, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class DBSCAN
extends weka.clusterers.AbstractClusterer
implements weka.core.OptionHandler, weka.core.TechnicalInformationHandler

Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported. More info:

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.

BibTeX:

 @inproceedings{Ester1996,
    author = {Martin Ester and Hans-Peter Kriegel and Joerg Sander and Xiaowei Xu},
    booktitle = {Second International Conference on Knowledge Discovery and Data Mining},
    editor = {Evangelos Simoudis and Jiawei Han and Usama M. Fayyad},
    pages = {226-231},
    publisher = {AAAI Press},
    title = {A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise},
    year = {1996}
 }
 

Valid options are:

 -E <double>
  epsilon (default = 0.9)

 -M <int>
  minPoints (default = 6)

 -I <String>
  index (database) used for DBSCAN (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)

 -D <String>
  distance-type (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)

Version:

$Revision: 8108 $

Author:

Matthias Schubert (schubert@dbs.ifi.lmu.de), Zhanna Melnikova-Albrecht (melnikov@cip.ifi.lmu.de), Rainer Holzmann (holzmann@cip.ifi.lmu.de)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DBSCAN()

Method Summary

Modifier and Type	Method and Description
void	buildClusterer(weka.core.Instances instances) Generate Clustering via DBSCAN
int	clusterInstance(weka.core.Instance instance) Classifies a given instance.
java.lang.String	distanceFunctionTipText() Returns the tip text for this property.
java.lang.String	epsilonTipText() Returns the tip text for this property
weka.core.Capabilities	getCapabilities() Returns default capabilities of the clusterer.
weka.core.DistanceFunction	getDistanceFunction() returns the distance function currently in use.
double	getEpsilon() Returns the value of epsilon
int	getMinPoints() Returns the value of minPoints
java.lang.String[]	getOptions() Gets the current option settings for the OptionHandler.
java.lang.String	getRevision() Returns the revision string.
weka.core.TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
java.lang.String	globalInfo() Returns a string describing this DataMining-Algorithm
java.util.Enumeration	listOptions() Returns an enumeration of all the available options..
static void	main(java.lang.String[] args) Main Method for testing DBSCAN
java.lang.String	minPointsTipText() Returns the tip text for this property
int	numberOfClusters() Returns the number of clusters.
void	setDistanceFunction(weka.core.DistanceFunction df) sets the distance function to use for nearest neighbour search.
void	setEpsilon(double epsilon) Sets a new value for epsilon
void	setMinPoints(int minPoints) Sets a new value for minPoints
void	setOptions(java.lang.String[] options) Sets the OptionHandler's options using the given list.
java.lang.String	toString() Returns a description of the clusterer

Methods inherited from class weka.clusterers.AbstractClusterer

debugTipText, distributionForInstance, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, postExecution, preExecution, run, runClusterer, setDebug, setDoNotCheckCapabilities

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

DBSCAN

public DBSCAN()

Method Detail

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the clusterer.

Specified by:

getCapabilities in interface weka.clusterers.Clusterer

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.clusterers.AbstractClusterer

Returns:

the capabilities of this clusterer

buildClusterer

public void buildClusterer(weka.core.Instances instances)
                    throws java.lang.Exception

Generate Clustering via DBSCAN

Specified by:

buildClusterer in interface weka.clusterers.Clusterer

Specified by:

buildClusterer in class weka.clusterers.AbstractClusterer

Parameters:

instances - The instances that need to be clustered

Throws:

java.lang.Exception - If clustering was not successful

clusterInstance

public int clusterInstance(weka.core.Instance instance)
                    throws java.lang.Exception

Classifies a given instance.

Specified by:

clusterInstance in interface weka.clusterers.Clusterer

Overrides:

clusterInstance in class weka.clusterers.AbstractClusterer

Parameters:

instance - The instance to be assigned to a cluster

Returns:

int The number of the assigned cluster as an integer

Throws:

java.lang.Exception - If instance could not be clustered successfully

numberOfClusters

public int numberOfClusters()
                     throws java.lang.Exception

Returns the number of clusters.

Specified by:

numberOfClusters in interface weka.clusterers.Clusterer

Specified by:

numberOfClusters in class weka.clusterers.AbstractClusterer

Returns:

int The number of clusters generated for a training dataset.

Throws:

java.lang.Exception - if number of clusters could not be returned successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration of all the available options..

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.clusterers.AbstractClusterer

Returns:

Enumeration An enumeration of all available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible).

Valid options are:

 -E <double>
  epsilon (default = 0.9)

 -M <int>
  minPoints (default = 6)

 -I <String>
  index (database) used for DBSCAN (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)

 -D <String>
  distance-type (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.clusterers.AbstractClusterer

Parameters:

options - The list of options as an array of strings

Throws:

java.lang.Exception - If an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current option settings for the OptionHandler.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.clusterers.AbstractClusterer

Returns:

String[] The list of current option settings as an array of strings

setMinPoints

public void setMinPoints(int minPoints)

Sets a new value for minPoints

Parameters:

minPoints - MinPoints

setEpsilon

public void setEpsilon(double epsilon)

Sets a new value for epsilon

Parameters:

epsilon - Epsilon

getEpsilon

public double getEpsilon()

Returns the value of epsilon

Returns:

double Epsilon

getMinPoints

public int getMinPoints()

Returns the value of minPoints

Returns:

int MinPoints

distanceFunctionTipText

public java.lang.String distanceFunctionTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getDistanceFunction

public weka.core.DistanceFunction getDistanceFunction()

returns the distance function currently in use.

Returns:

the distance function

setDistanceFunction

public void setDistanceFunction(weka.core.DistanceFunction df)
                         throws java.lang.Exception

sets the distance function to use for nearest neighbour search.

Parameters:

df - the new distance function to use

Throws:

java.lang.Exception - if instances cannot be processed

epsilonTipText

public java.lang.String epsilonTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

minPointsTipText

public java.lang.String minPointsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

globalInfo

public java.lang.String globalInfo()

Returns a string describing this DataMining-Algorithm

Returns:

String Information for the gui-explorer

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

toString

public java.lang.String toString()

Returns a description of the clusterer

Overrides:

toString in class java.lang.Object

Returns:

a string representation of the clusterer

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.clusterers.AbstractClusterer

Returns:

the revision

main

public static void main(java.lang.String[] args)

Main Method for testing DBSCAN

Parameters:

args - Valid parameters are: 'E' epsilon (default = 0.9); 'M' minPoints (default = 6); 'I' index-type (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase); 'D' distance-type (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject);