clusterevaluation.html

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public <B>ClusterEvaluation</B>()</PRE>
<DL>
<DD>Constructor. Sets defaults for each member variable. Default Clusterer
 is EM.
<P>
</DL>

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<B>Method Detail</B></FONT></TD>
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<A NAME="setClusterer(weka.clusterers.Clusterer)"><!-- --></A><H3>
setClusterer</H3>
<PRE>
public void <B>setClusterer</B>(<A HREF="../../weka/clusterers/Clusterer.html" title="class in weka.clusterers">Clusterer</A>&nbsp;clusterer)</PRE>
<DL>
<DD>set the clusterer
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>clusterer</CODE> - the clusterer to use</DL>
</DD>
</DL>
<HR>

<A NAME="clusterResultsToString()"><!-- --></A><H3>
clusterResultsToString</H3>
<PRE>
public java.lang.String <B>clusterResultsToString</B>()</PRE>
<DL>
<DD>return the results of clustering.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>a string detailing the results of clustering a data set</DL>
</DD>
</DL>
<HR>

<A NAME="getNumClusters()"><!-- --></A><H3>
getNumClusters</H3>
<PRE>
public int <B>getNumClusters</B>()</PRE>
<DL>
<DD>Return the number of clusters found for the most recent call to
 evaluateClusterer
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the number of clusters found</DL>
</DD>
</DL>
<HR>

<A NAME="getClusterAssignments()"><!-- --></A><H3>
getClusterAssignments</H3>
<PRE>
public double[] <B>getClusterAssignments</B>()</PRE>
<DL>
<DD>Return an array of cluster assignments corresponding to the most
 recent set of instances clustered.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>an array of cluster assignments</DL>
</DD>
</DL>
<HR>

<A NAME="getClassesToClusters()"><!-- --></A><H3>
getClassesToClusters</H3>
<PRE>
public int[] <B>getClassesToClusters</B>()</PRE>
<DL>
<DD>Return the array (ordered by cluster number) of minimum error class to
 cluster mappings
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>an array of class to cluster mappings</DL>
</DD>
</DL>
<HR>

<A NAME="getLogLikelihood()"><!-- --></A><H3>
getLogLikelihood</H3>
<PRE>
public double <B>getLogLikelihood</B>()</PRE>
<DL>
<DD>Return the log likelihood corresponding to the most recent
 set of instances clustered.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>a <code>double</code> value</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateClusterer(weka.core.Instances)"><!-- --></A><H3>
evaluateClusterer</H3>
<PRE>
public void <B>evaluateClusterer</B>(<A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;test)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Evaluate the clusterer on a set of instances. Calculates clustering
 statistics and stores cluster assigments for the instances in
 m_clusterAssignments
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>test</CODE> - the set of instances to cluster
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="evaluateClusterer(weka.clusterers.Clusterer, java.lang.String[])"><!-- --></A><H3>
evaluateClusterer</H3>
<PRE>
public static java.lang.String <B>evaluateClusterer</B>(<A HREF="../../weka/clusterers/Clusterer.html" title="class in weka.clusterers">Clusterer</A>&nbsp;clusterer,
                                                 java.lang.String[]&nbsp;options)
                                          throws java.lang.Exception</PRE>
<DL>
<DD>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.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>clusterer</CODE> - machine learning clusterer<DD><CODE>options</CODE> - the array of string containing the options
<DT><B>Returns:</B><DD>a string describing the results
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if model could not be evaluated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="crossValidateModel(weka.clusterers.DensityBasedClusterer, weka.core.Instances, int, java.util.Random)"><!-- --></A><H3>
crossValidateModel</H3>
<PRE>
public static double <B>crossValidateModel</B>(<A HREF="../../weka/clusterers/DensityBasedClusterer.html" title="class in weka.clusterers">DensityBasedClusterer</A>&nbsp;clusterer,
                                        <A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                                        int&nbsp;numFolds,
                                        java.util.Random&nbsp;random)
                                 throws java.lang.Exception</PRE>
<DL>
<DD>Perform a cross-validation for DensityBasedClusterer on a set of instances.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>clusterer</CODE> - the clusterer to use<DD><CODE>data</CODE> - the training data<DD><CODE>numFolds</CODE> - number of folds of cross validation to perform<DD><CODE>random</CODE> - random number seed for cross-validation
<DT><B>Returns:</B><DD>the cross-validated log-likelihood
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if an error occurs</DL>
</DD>
</DL>
<HR>

<A NAME="crossValidateModel(java.lang.String, weka.core.Instances, int, java.lang.String[], java.util.Random)"><!-- --></A><H3>
crossValidateModel</H3>
<PRE>
public static java.lang.String <B>crossValidateModel</B>(java.lang.String&nbsp;clustererString,
                                                  <A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                                                  int&nbsp;numFolds,
                                                  java.lang.String[]&nbsp;options,
                                                  java.util.Random&nbsp;random)
                                           throws java.lang.Exception</PRE>
<DL>
<DD>Performs a cross-validation 
 for a DensityBasedClusterer clusterer on a set of instances.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>clustererString</CODE> - a string naming the class of the clusterer<DD><CODE>data</CODE> - the data on which the cross-validation is to be 
 performed<DD><CODE>numFolds</CODE> - the number of folds for the cross-validation<DD><CODE>options</CODE> - the options to the clusterer<DD><CODE>random</CODE> - a random number generator
<DT><B>Returns:</B><DD>a string containing the cross validated log likelihood
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if a clusterer could not be generated</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;args)</PRE>
<DL>
<DD>Main method for testing this class.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>args</CODE> - the options</DL>
</DD>
</DL>
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