Package weka.clusterers

Class MakeDensityBasedClusterer

java.lang.Object

weka.clusterers.AbstractClusterer

weka.clusterers.AbstractDensityBasedClusterer

weka.clusterers.MakeDensityBasedClusterer

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, DensityBasedClusterer, NumberOfClustersRequestable, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class MakeDensityBasedClusterer
extends AbstractDensityBasedClusterer
implements NumberOfClustersRequestable, OptionHandler, WeightedInstancesHandler

Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster produced by the wrapped clusterer. Supports the NumberOfClustersRequestable interface only if the wrapped Clusterer does.

Valid options are:

 -M <num>
  minimum allowable standard deviation for normal density computation 
  (default 1e-6)
 

 -W <clusterer name>
  Clusterer to wrap.
  (default weka.clusterers.SimpleKMeans)
 

 Options specific to clusterer weka.clusterers.SimpleKMeans:
 

 -N <num>
  number of clusters.
  (default 2).
 

 -V
  Display std. deviations for centroids.
 

 -M
  Replace missing values with mean/mode.
 

 -S <num>
  Random number seed.
  (default 10)
 

Options after "--" are passed on to the base clusterer.

Version:

$Revision$

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor	Description
MakeDensityBasedClusterer()	Default constructor.
MakeDensityBasedClusterer(Clusterer toWrap)	Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.

Method Summary

Modifier and Type	Method	Description
void	buildClusterer(Instances data)	Builds a clusterer for a set of instances.
String	clustererTipText()	Returns the tip text for this property
double[]	clusterPriors()	Returns the cluster priors.
Capabilities	getCapabilities()	Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).
Clusterer	getClusterer()	Gets the clusterer being wrapped.
double	getMinStdDev()	Get the minimum allowable standard deviation.
String[]	getOptions()	Gets the current settings of the clusterer.
String	getRevision()	Returns the revision string.
String	globalInfo()	Returns a string describing classifier
Enumeration<Option>	listOptions()	Returns an enumeration describing the available options..
double[]	logDensityPerClusterForInstance(Instance inst)	Computes the log of the conditional density (per cluster) for a given instance.
static void	main(String[] argv)	Main method for testing this class.
String	minStdDevTipText()	Returns the tip text for this property
int	numberOfClusters()	Returns the number of clusters.
void	setClusterer(Clusterer toWrap)	Sets the clusterer to wrap.
void	setMinStdDev(double m)	Set the minimum value for standard deviation when calculating normal density.
void	setNumClusters(int n)	Set the number of clusters to generate.
void	setOptions(String[] options)	Parses a given list of options.
String	toString()	Returns a description of the clusterer.

Methods inherited from class weka.clusterers.AbstractDensityBasedClusterer

distributionForInstance, logDensityForInstance, logJointDensitiesForInstance, makeCopies

Methods inherited from class weka.clusterers.AbstractClusterer

clusterInstance, debugTipText, 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.clusterers.Clusterer

clusterInstance

Constructor Detail

MakeDensityBasedClusterer

public MakeDensityBasedClusterer()

Default constructor.

MakeDensityBasedClusterer

public MakeDensityBasedClusterer(Clusterer toWrap)

Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.

Parameters:

toWrap - the clusterer to wrap around

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

setNumClusters

public void setNumClusters(int n)
                    throws Exception

Set the number of clusters to generate.

Specified by:

setNumClusters in interface NumberOfClustersRequestable

Parameters:

n - the number of clusters to generate

Throws:

Exception - if the wrapped clusterer has not been set, or if the wrapped clusterer does not implement this facility.

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).

Specified by:

getCapabilities in interface CapabilitiesHandler

Specified by:

getCapabilities in interface Clusterer

Overrides:

getCapabilities in class AbstractClusterer

Returns:

the capabilities of this clusterer

See Also:

Capabilities

buildClusterer

public void buildClusterer(Instances data)
                    throws Exception

Builds a clusterer for a set of instances.

Specified by:

buildClusterer in interface Clusterer

Specified by:

buildClusterer in class AbstractClusterer

Parameters:

data - the instances to train the clusterer with

Throws:

Exception - if the clusterer hasn't been set or something goes wrong

clusterPriors

public double[] clusterPriors()

Returns the cluster priors.

Specified by:

clusterPriors in interface DensityBasedClusterer

Specified by:

clusterPriors in class AbstractDensityBasedClusterer

Returns:

the cluster priors

logDensityPerClusterForInstance

public double[] logDensityPerClusterForInstance(Instance inst)
                                         throws Exception

Computes the log of the conditional density (per cluster) for a given instance.

Specified by:

logDensityPerClusterForInstance in interface DensityBasedClusterer

Specified by:

logDensityPerClusterForInstance in class AbstractDensityBasedClusterer

Parameters:

inst - the instance to compute the density for

Returns:

an array containing the estimated densities

Throws:

Exception - if the density could not be computed 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:

the number of clusters generated for a training dataset.

Throws:

Exception - if number of clusters could not be returned successfully

toString

public String toString()

Returns a description of the clusterer.

Overrides:

toString in class Object

Returns:

a string containing a description of the clusterer

clustererTipText

public String clustererTipText()

Returns the tip text for this property

Returns:

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

setClusterer

public void setClusterer(Clusterer toWrap)

Sets the clusterer to wrap.

Parameters:

toWrap - the clusterer

getClusterer

public Clusterer getClusterer()

Gets the clusterer being wrapped.

Returns:

the clusterer

minStdDevTipText

public String minStdDevTipText()

Returns the tip text for this property

Returns:

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

setMinStdDev

public void setMinStdDev(double m)

Set the minimum value for standard deviation when calculating normal density. Reducing this value can help prevent arithmetic overflow resulting from multiplying large densities (arising from small standard deviations) when there are many singleton or near singleton values.

Parameters:

m - minimum value for standard deviation

getMinStdDev

public double getMinStdDev()

Get the minimum allowable standard deviation.

Returns:

the minumum allowable standard deviation

listOptions

public Enumeration<Option> listOptions()

Returns an enumeration describing the available options..

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClusterer

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -M <num>
  minimum allowable standard deviation for normal density computation 
  (default 1e-6)
 

 -W <clusterer name>
  Clusterer to wrap.
  (default weka.clusterers.SimpleKMeans)
 

 Options specific to clusterer weka.clusterers.SimpleKMeans:
 

 -N <num>
  number of clusters.
  (default 2).
 

 -V
  Display std. deviations for centroids.
 

 -M
  Replace missing values with mean/mode.
 

 -S <num>
  Random number seed.
  (default 10)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClusterer

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 settings of the clusterer.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClusterer

Returns:

an array of strings suitable for passing to setOptions()

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[] argv)

Main method for testing this class.

Parameters:

argv - the options