weka.clusterers

Class DBSCAN

java.lang.Object

weka.clusterers.AbstractClusterer

weka.clusterers.DBSCAN

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class DBSCAN
extends AbstractClusterer
implements OptionHandler, 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: 9434 $

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(Instances instances) Generate Clustering via DBSCAN
int	clusterInstance(Instance instance) Classifies a given instance.
String	database_distanceTypeTipText() Returns the tip text for this property
String	database_TypeTipText() Returns the tip text for this property
Database	databaseForName(String database_Type, Instances instances) Returns a new Class-Instance of the specified database
DataObject	dataObjectForName(String database_distanceType, Instance instance, String key, Database database) Returns a new Class-Instance of the specified database
String	epsilonTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the clusterer.
String	getDatabase_distanceType() Returns the distance-type
String	getDatabase_Type() Returns the type of the used index (database)
double	getEpsilon() Returns the value of epsilon
int	getMinPoints() Returns the value of minPoints
String[]	getOptions() Gets the current option settings for the OptionHandler.
String	getRevision() Returns the revision string.
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.
String	globalInfo() Returns a string describing this DataMining-Algorithm
Enumeration	listOptions() Returns an enumeration of all the available options..
static void	main(String[] args) Main Method for testing DBSCAN
String	minPointsTipText() Returns the tip text for this property
int	numberOfClusters() Returns the number of clusters.
void	setDatabase_distanceType(String database_distanceType) Sets a new distance-type
void	setDatabase_Type(String database_Type) Sets a new database-type
void	setEpsilon(double epsilon) Sets a new value for epsilon
void	setMinPoints(int minPoints) Sets a new value for minPoints
void	setOptions(String[] options) Sets the OptionHandler's options using the given list.
String	toString() Returns a description of the clusterer

Methods inherited from class weka.clusterers.AbstractClusterer

distributionForInstance, forName, makeCopies, makeCopy

Methods inherited from class java.lang.Object

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

Constructor Detail

DBSCAN

public DBSCAN()

Method Detail

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the clusterer.

Specified by:

getCapabilities in interface Clusterer

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClusterer

Returns:

the capabilities of this clusterer

See Also:

Capabilities

buildClusterer

public void buildClusterer(Instances instances)
                    throws Exception

Generate Clustering via DBSCAN

Specified by:

buildClusterer in interface Clusterer

Specified by:

buildClusterer in class AbstractClusterer

Parameters:

instances - The instances that need to be clustered

Throws:

Exception - If clustering was not successful

clusterInstance

public int clusterInstance(Instance instance)
                    throws Exception

Classifies a given instance.

Specified by:

clusterInstance in interface Clusterer

Overrides:

clusterInstance in class AbstractClusterer

Parameters:

instance - The instance to be assigned to a cluster

Returns:

int The number of the assigned cluster as an integer

Throws:

Exception - If instance could not be clustered successfully

numberOfClusters

public int numberOfClusters()
                     throws Exception

Returns the number of clusters.

Specified by:

numberOfClusters in interface Clusterer

Specified by:

numberOfClusters in class AbstractClusterer

Returns:

int The number of clusters generated for a training dataset.

Throws:

Exception - if number of clusters could not be returned successfully

listOptions

public Enumeration listOptions()

Returns an enumeration of all the available options..

Specified by:

listOptions in interface OptionHandler

Returns:

Enumeration An enumeration of all available options.

setOptions

public void setOptions(String[] options)
                throws 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 OptionHandler

Parameters:

options - The list of options as an array of strings

Throws:

Exception - If an option is not supported

getOptions

public String[] getOptions()

Gets the current option settings for the OptionHandler.

Specified by:

getOptions in interface OptionHandler

Returns:

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

databaseForName

public Database databaseForName(String database_Type,
                                Instances instances)

Returns a new Class-Instance of the specified database

Parameters:

database_Type - String of the specified database

instances - Instances that were delivered from WEKA

Returns:

Database New constructed Database

dataObjectForName

public DataObject dataObjectForName(String database_distanceType,
                                    Instance instance,
                                    String key,
                                    Database database)

Returns a new Class-Instance of the specified database

Parameters:

database_distanceType - String of the specified distance-type

instance - The original instance that needs to hold by this DataObject

key - Key for this DataObject

database - Link to the database

Returns:

DataObject New constructed DataObject

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

getDatabase_distanceType

public String getDatabase_distanceType()

Returns the distance-type

Returns:

String Distance-type

getDatabase_Type

public String getDatabase_Type()

Returns the type of the used index (database)

Returns:

String Index-type

setDatabase_distanceType

public void setDatabase_distanceType(String database_distanceType)

Sets a new distance-type

Parameters:

database_distanceType - The new distance-type

setDatabase_Type

public void setDatabase_Type(String database_Type)

Sets a new database-type

Parameters:

database_Type - The new database-type

epsilonTipText

public 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 String minPointsTipText()

Returns the tip text for this property

Returns:

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

database_TypeTipText

public String database_TypeTipText()

Returns the tip text for this property

Returns:

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

database_distanceTypeTipText

public String database_distanceTypeTipText()

Returns the tip text for this property

Returns:

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

globalInfo

public String globalInfo()

Returns a string describing this DataMining-Algorithm

Returns:

String Information for the gui-explorer

getTechnicalInformation

public 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 TechnicalInformationHandler

Returns:

the technical information about this class

toString

public String toString()

Returns a description of the clusterer

Overrides:

toString in class Object

Returns:

a string representation of the clusterer

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClusterer

Returns:

the revision

main

public static void main(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);