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Class weka.clusterers.ClusterEvaluation 

java.lang.Object
   |
   +----weka.clusterers.ClusterEvaluation
public class ClusterEvaluation
extends Object
Class for evaluating clustering models.

Valid options are:

-t
Specify the training file.

-T
Specify the test file to apply clusterer to.

-d
Specify output file.

-l
Specifiy input file.

-p
Output predictions. Predictions are for the training file if only the training file is specified, otherwise they are for the test file. The range specifies attribute values to be output with the predictions. Use '-p 0' for none.

-x
Set the number of folds for a cross validation of the training data. Cross validation can only be done for distribution clusterers and will be performed if the test file is missing.

-c
Set the class attribute. If set, then class based evaluation of clustering is performed.

Author:
Mark Hall (mhall@cs.waikato.ac.nz)

Constructor Index

o ClusterEvaluation()
Constructor.

Method Index

o clusterResultsToString()
return the results of clustering.
o crossValidateModel(String, Instances, int, String[])
Performs a cross-validation for a distribution clusterer on a set of instances.
o evaluateClusterer(Clusterer, String[])
Evaluates a clusterer with the options given in an array of strings.
o evaluateClusterer(Instances)
Evaluate the clusterer on a set of instances.
o getClassesToClusters()
Return the array (ordered by cluster number) of minimum error class to cluster mappings
o getClusterAssignments()
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
o getNumClusters()
Return the number of clusters found for the most recent call to evaluateClusterer
o main(String[])
Main method for testing this class.
o setClusterer(Clusterer)
set the clusterer
o setDoXval(boolean)
set whether or not to do cross validation
o setFolds(int)
set the number of folds to use for cross validation
o setSeed(int)
set the seed to use for cross validation

Constructors

o ClusterEvaluation 
 public ClusterEvaluation()
Constructor. Sets defaults for each member variable. Default Clusterer is EM.

Methods

o setClusterer 
 public void setClusterer(Clusterer clusterer)
set the clusterer

Parameters:
clusterer - the clusterer to use
o setDoXval 
 public void setDoXval(boolean x)
set whether or not to do cross validation

Parameters:
x - true if cross validation is to be done
o setFolds 
 public void setFolds(int folds)
set the number of folds to use for cross validation

Parameters:
folds - the number of folds
o setSeed 
 public void setSeed(int s)
set the seed to use for cross validation

Parameters:
s - the seed.
o clusterResultsToString 
 public String clusterResultsToString()
return the results of clustering.

Returns:
a string detailing the results of clustering a data set
o getNumClusters 
 public int getNumClusters()
Return the number of clusters found for the most recent call to evaluateClusterer

Returns:
the number of clusters found
o getClusterAssignments 
 public double[] getClusterAssignments()
Return an array of cluster assignments corresponding to the most recent set of instances clustered.

Returns:
an array of cluster assignments
o getClassesToClusters 
 public int[] getClassesToClusters()
Return the array (ordered by cluster number) of minimum error class to cluster mappings

Returns:
an array of class to cluster mappings
o evaluateClusterer 
 public void evaluateClusterer(Instances test) throws Exception
Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores cluster assigments for the instances in m_clusterAssignments

Parameters:
test - the set of instances to cluster
Throws: Exception
if something goes wrong
o evaluateClusterer 
 public static String evaluateClusterer(Clusterer clusterer,
                                        String options[]) throws Exception
Evaluates a clusterer with the options given in an array of strings. It takes the string indicated by "-t" as training file, the string indicated by "-T" as test file. If the test file is missing, a stratified ten-fold cross-validation is performed (distribution clusterers only). Using "-x" you can change the number of folds to be used, and using "-s" the random seed. If the "-p" option is present it outputs the classification for each test instance. If you provide the name of an object file using "-l", a clusterer will be loaded from the given file. If you provide the name of an object file using "-d", the clusterer built from the training data will be saved to the given file.

Parameters:
clusterer - machine learning clusterer
options - the array of string containing the options
Returns:
a string describing the results
Throws: Exception
if model could not be evaluated successfully
o crossValidateModel 
 public static String crossValidateModel(String clustererString,
                                         Instances data,
                                         int numFolds,
                                         String options[]) throws Exception
Performs a cross-validation for a distribution clusterer on a set of instances.

Parameters:
clustererString - a string naming the class of the clusterer
data - the data on which the cross-validation is to be performed
numFolds - the number of folds for the cross-validation
options - the options to the clusterer
Returns:
a string containing the cross validated log likelihood
Throws: Exception
if a clusterer could not be generated
o main 
 public static void main(String args[])
Main method for testing this class.

Parameters:
args - the options 
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