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java.lang.Objectweka.clusterers.Clusterer
weka.clusterers.DBScan
public class DBScan
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)
Constructor Summary | |
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DBScan()
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Method Summary | |
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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. |
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.Clusterer |
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distributionForInstance, forName, makeCopies, makeCopy |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public DBScan()
Method Detail |
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public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Clusterer
Capabilities
public void buildClusterer(Instances instances) throws java.lang.Exception
buildClusterer
in class Clusterer
instances
- The instances that need to be clustered
java.lang.Exception
- If clustering was not successfulpublic int clusterInstance(Instance instance) throws java.lang.Exception
clusterInstance
in class Clusterer
instance
- The instance to be assigned to a cluster
java.lang.Exception
- If instance could not be clustered
successfullypublic int numberOfClusters() throws java.lang.Exception
numberOfClusters
in class Clusterer
java.lang.Exception
- if number of clusters could not be returned
successfullypublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
setOptions
in interface OptionHandler
options
- The list of options as an array of strings
java.lang.Exception
- If an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
public Database databaseForName(java.lang.String database_Type, Instances instances)
database_Type
- String of the specified databaseinstances
- Instances that were delivered from WEKA
public DataObject dataObjectForName(java.lang.String database_distanceType, Instance instance, java.lang.String key, Database database)
database_distanceType
- String of the specified distance-typeinstance
- The original instance that needs to hold by this DataObjectkey
- Key for this DataObjectdatabase
- Link to the database
public void setMinPoints(int minPoints)
minPoints
- MinPointspublic void setEpsilon(double epsilon)
epsilon
- Epsilonpublic double getEpsilon()
public int getMinPoints()
public java.lang.String getDatabase_distanceType()
public java.lang.String getDatabase_Type()
public void setDatabase_distanceType(java.lang.String database_distanceType)
database_distanceType
- The new distance-typepublic void setDatabase_Type(java.lang.String database_Type)
database_Type
- The new database-typepublic java.lang.String epsilonTipText()
public java.lang.String minPointsTipText()
public java.lang.String database_TypeTipText()
public java.lang.String database_distanceTypeTipText()
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] args)
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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