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EMClustering (Java Machine Learning Library 0.0.11)
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net.sf.javaml.clustering</FONT>
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Class EMClustering</H2>
<PRE>
java.lang.Object
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><B>net.sf.javaml.clustering.EMClustering</B>
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<DL>
<DT><B>All Implemented Interfaces:</B> <DD><A HREF="../../../../net/sf/javaml/clustering/Clusterer.html" title="interface in net.sf.javaml.clustering">Clusterer</A></DD>
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<DT><PRE>public class <B>EMClustering</B><DT>extends java.lang.Object<DT>implements <A HREF="../../../../net/sf/javaml/clustering/Clusterer.html" title="interface in net.sf.javaml.clustering">Clusterer</A></DL>
</PRE>
<P>
Simple EM (expectation maximisation) class.<br/> <br/> EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. EM can decide how many clusters to create by cross validation, or you may specify apriori how many clusters to generate.<br/> <br/> The cross validation performed to determine the number of clusters is done in the following steps:<br/> 1. the number of clusters is set to 1<br/> 2. the training set is split randomly into 10 folds.<br/> 3. EM is performed 10 times using the 10 folds the usual CV way.<br/> 4. the loglikelihood is averaged over all 10 results.<br/> 5. if loglikelihood has increased the number of clusters is increased by 1 and the program continues at step 2. <br/> <br/> The number of folds is fixed to 10, as long as the number of instances in the training set is not smaller 10. If this is the case the number of folds is set equal to the number of instances. <p/>
<P>
<P>
<DL>
<DT><B>Author:</B></DT>
<DD>Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Thomas Abeel</DD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/EMClustering.html#EMClustering()">EMClustering</A></B>()</CODE>
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default constructor</TD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/EMClustering.html#EMClustering(int, int, net.sf.javaml.distance.DistanceMeasure)">EMClustering</A></B>(int numClusters,
int m_max_iterations,
<A HREF="../../../../net/sf/javaml/distance/DistanceMeasure.html" title="interface in net.sf.javaml.distance">DistanceMeasure</A> dm)</CODE>
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XXX DOC</TD>
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<CODE> <A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A>[]</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/EMClustering.html#executeClustering(net.sf.javaml.core.Dataset)">executeClustering</A></B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</CODE>
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This method will execute the clustering algorithm on a particular dataset.</TD>
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<TD><CODE>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</CODE></TD>
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<A NAME="EMClustering()"><!-- --></A><H3>
EMClustering</H3>
<PRE>
public <B>EMClustering</B>()</PRE>
<DL>
<DD>default constructor
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<A NAME="EMClustering(int, int, net.sf.javaml.distance.DistanceMeasure)"><!-- --></A><H3>
EMClustering</H3>
<PRE>
public <B>EMClustering</B>(int numClusters,
int m_max_iterations,
<A HREF="../../../../net/sf/javaml/distance/DistanceMeasure.html" title="interface in net.sf.javaml.distance">DistanceMeasure</A> dm)</PRE>
<DL>
<DD>XXX DOC
<P>
<DL>
<DT><B>Parameters:</B><DD><CODE>numClusters</CODE> - <DD><CODE>m_max_iterations</CODE> - <DD><CODE>DistanceMeasure</CODE> - dm</DL>
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<A NAME="executeClustering(net.sf.javaml.core.Dataset)"><!-- --></A><H3>
executeClustering</H3>
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public <A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A>[] <B>executeClustering</B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</PRE>
<DL>
<DD><B>Description copied from interface: <CODE><A HREF="../../../../net/sf/javaml/clustering/Clusterer.html#executeClustering(net.sf.javaml.core.Dataset)">Clusterer</A></CODE></B></DD>
<DD>This method will execute the clustering algorithm on a particular dataset. The result will be an array of Dataset where each dataset is a cluster.
<P>
<DD><DL>
<DT><B>Specified by:</B><DD><CODE><A HREF="../../../../net/sf/javaml/clustering/Clusterer.html#executeClustering(net.sf.javaml.core.Dataset)">executeClustering</A></CODE> in interface <CODE><A HREF="../../../../net/sf/javaml/clustering/Clusterer.html" title="interface in net.sf.javaml.clustering">Clusterer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the dataset on which to execute the clustering.
<DT><B>Returns:</B><DD>the different clusters obtained by this clustering algorithm. Each cluster is represented as a seperate dataset.</DL>
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