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XMeans (Java Machine Learning Library 0.0.11)
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net.sf.javaml.clustering</FONT>
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Class XMeans</H2>
<PRE>
java.lang.Object
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><B>net.sf.javaml.clustering.XMeans</B>
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<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>XMeans</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>
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<P>
Cluster data using the X-means algorithm.<br/> <br/> X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures.<br/> <br/> For more information see:<br/> <br/> Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000. <p/>
<P>
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<DL>
<DT><B>Author:</B></DT>
<DD>Gabi Schmidberger (gabi@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Malcolm Ware (mfw4@cs.waikato.ac.nz), Thomas Abeel</DD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/XMeans.html#XMeans()">XMeans</A></B>()</CODE>
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default constructor</TD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/XMeans.html#XMeans(int, int, int, net.sf.javaml.distance.DistanceMeasure)">XMeans</A></B>(int m_MaxIterations,
int m_MinNumClusters,
int m_MaxNumClusters,
<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/XMeans.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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Generates the X-Means clusterer.</TD>
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<A NAME="XMeans()"><!-- --></A><H3>
XMeans</H3>
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public <B>XMeans</B>()</PRE>
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<DD>default constructor
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<A NAME="XMeans(int, int, int, net.sf.javaml.distance.DistanceMeasure)"><!-- --></A><H3>
XMeans</H3>
<PRE>
public <B>XMeans</B>(int m_MaxIterations,
int m_MinNumClusters,
int m_MaxNumClusters,
<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>m_MaxIterations</CODE> - <DD><CODE>m_MinNumClusters</CODE> - <DD><CODE>m_MaxNumClusters</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>Generates the X-Means clusterer.
<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> - set of instances serving as training data
<DT><B>Returns:</B><DD>the different clusters obtained by this clustering algorithm. Each cluster is represented as a seperate dataset.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if the clusterer has not been generated successfully</DL>
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