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: 15519 $
Author:
Richard Kirkby (rkirkby@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • 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 Details

    • 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:

    • 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