Package weka.classifiers.meta

Class ClassificationViaClustering

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler
    public class ClassificationViaClustering
extends Classifier
    A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.

    Note: at prediction time, a missing value is returned if no cluster is found for the instance.

    The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.

    Valid options are: 

     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
     -W
  Full name of clusterer.
  (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)
    Version:
    $Revision: 1.5 $
    Author:
    fracpete (fracpete at waikato dot ac dot nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • ClassificationViaClustering

        public ClassificationViaClustering()
        default constructor

    • Method Detail

      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing classifier
        Returns:
        a description suitable for displaying in the explorer/experimenter gui

      • listOptions

        public java.util.Enumeration listOptions()
        Gets an enumeration describing the available options.
        Specified by:
        listOptions in interface OptionHandler
        Overrides:
        listOptions in class Classifier
        Returns:
        an enumeration of all the available options.

      • getOptions

        public java.lang.String[] getOptions()
        returns the options of the current setup
        Specified by:
        getOptions in interface OptionHandler
        Overrides:
        getOptions in class Classifier
        Returns:
        the current options

      • setOptions

        public void setOptions​(java.lang.String[] options)
                throws java.lang.Exception
        Parses the options for this object.

        Valid options are: 

         -D
  If set, classifier is run in debug mode and
  may output additional info to the console
         -W
  Full name of clusterer.
  (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 Classifier
        Parameters:
        options - the options to use
        Throws:
        java.lang.Exception - if setting of options fails

      • clustererTipText

        public java.lang.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 value)
        Set the base clusterer.
        Parameters:
        value - the clusterer to use.

      • getClusterer

        public Clusterer getClusterer()
        Get the clusterer used as the base learner.
        Returns:
        the current clusterer

      • classifyInstance

        public double classifyInstance​(Instance instance)
                        throws java.lang.Exception
        Classifies the given test instance.
        Overrides:
        classifyInstance in class Classifier
        Parameters:
        instance - the instance to be classified
        Returns:
        the predicted most likely class for the instance or Instance.missingValue() if no prediction is made
        Throws:
        java.lang.Exception - if an error occurred during the prediction

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        builds the classifier
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        data - the training instances
        Throws:
        java.lang.Exception - if something goes wrong

      • toString

        public java.lang.String toString()
        Returns a string representation of the classifier.
        Overrides:
        toString in class java.lang.Object
        Returns:
        a string representation of the classifier.

      • main

        public static void main​(java.lang.String[] args)
        Runs the classifier with the given options
        Parameters:
        args - the commandline options