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## weka.clusterers
Class DBScan

    java.lang.Object
      weka.clusterers.AbstractClusterer
          weka.clusterers.DBScan

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Clusterer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class DBScan

extends AbstractClusterer
implements OptionHandler, TechnicalInformationHandler

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.EuclidianDataObject)

Version:
  $Revision: 5538 $
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
`DBScan()`

Method Summary
`void` | `buildClusterer(Instances instances)` Generate Clustering via DBScan
`int` | `clusterInstance(Instance instance)` Classifies a given instance.
`java.lang.String` | `database_distanceTypeTipText()` Returns the tip text for this property
`java.lang.String` | `database_TypeTipText()` Returns the tip text for this property
`Database` | `databaseForName(java.lang.String database_Type, Instances instances)` Returns a new Class-Instance of the specified database
`DataObject` | `dataObjectForName(java.lang.String database_distanceType, Instance instance, java.lang.String key, Database database)` Returns a new Class-Instance of the specified database
`java.lang.String` | `epsilonTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the clusterer.
`java.lang.String` | `getDatabase_distanceType()` Returns the distance-type
`java.lang.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
`java.lang.String[]` | `getOptions()` Gets the current option settings for the OptionHandler.
`java.lang.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.
`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` | `setDatabase_distanceType(java.lang.String database_distanceType)` Sets a new distance-type
`void` | `setDatabase_Type(java.lang.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(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
`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 java.lang.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:
    `java.lang.Exception` - If clustering was not successful

### clusterInstance

    public int clusterInstance(Instance instance)
                        throws java.lang.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:
    `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 `Clusterer`
  Specified by:
    `numberOfClusters` in class `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 `OptionHandler`
  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.EuclidianDataObject)

  Specified by:
    `setOptions` in interface `OptionHandler`
  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 `OptionHandler`
  Returns:
    String[] The list of current option settings as an array of strings

### databaseForName

    public Database databaseForName(java.lang.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(java.lang.String database_distanceType,
                                        Instance instance,
                                        java.lang.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 java.lang.String getDatabase_distanceType()

  Returns the distance-type

  Returns:
    String Distance-type

### getDatabase_Type

    public java.lang.String getDatabase_Type()

  Returns the type of the used index (database)

  Returns:
    String Index-type

### setDatabase_distanceType

    public void setDatabase_distanceType(java.lang.String database_distanceType)

  Sets a new distance-type

  Parameters:
    `database_distanceType` - The new distance-type

### setDatabase_Type

    public void setDatabase_Type(java.lang.String database_Type)

  Sets a new database-type

  Parameters:
    `database_Type` - The new database-type

### 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

### database_TypeTipText

    public java.lang.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 java.lang.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 java.lang.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 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 `RevisionHandler`
  Overrides:
    `getRevision` in class `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.EuclidianDataObject);

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