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</TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../weka/classifiers/xml/package-summary.html"><B>PREV PACKAGE</B></A> <A HREF="../../weka/clusterers/forOPTICSAndDBScan/Databases/package-summary.html"><B>NEXT PACKAGE</B></A></FONT></TD><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../index.html?weka/clusterers/package-summary.html" target="_top"><B>FRAMES</B></A> <A HREF="package-summary.html" target="_top"><B>NO FRAMES</B></A> <SCRIPT type="text/javascript"> <!-- if(window==top) { document.writeln('<A HREF="../../allclasses-noframe.html"><B>All Classes</B></A>'); } //--></SCRIPT><NOSCRIPT> <A HREF="../../allclasses-noframe.html"><B>All Classes</B></A></NOSCRIPT></FONT></TD></TR></TABLE><A NAME="skip-navbar_top"></A><!-- ========= END OF TOP NAVBAR ========= --><HR><H2>Package weka.clusterers</H2><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Interface Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/NumberOfClustersRequestable.html" title="interface in weka.clusterers">NumberOfClustersRequestable</A></B></TD><TD>Interface to a clusterer that can generate a requested number of clusters</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/UpdateableClusterer.html" title="interface in weka.clusterers">UpdateableClusterer</A></B></TD><TD>Interface to incremental cluster models that can learn using one instance at a time.</TD></TR></TABLE> <P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Class Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/CheckClusterer.html" title="class in weka.clusterers">CheckClusterer</A></B></TD><TD>Class for examining the capabilities and finding problems with clusterers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/Clusterer.html" title="class in weka.clusterers">Clusterer</A></B></TD><TD>Abstract clusterer.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/ClusterEvaluation.html" title="class in weka.clusterers">ClusterEvaluation</A></B></TD><TD>Class for evaluating clustering models.<p/> Valid options are: <p/> -t name of the training file <br/> Specify the training file.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/Cobweb.html" title="class in weka.clusterers">Cobweb</A></B></TD><TD>Class implementing the Cobweb and Classit clustering algorithms.<br/> <br/> Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/DBScan.html" title="class in weka.clusterers">DBScan</A></B></TD><TD>Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/DensityBasedClusterer.html" title="class in weka.clusterers">DensityBasedClusterer</A></B></TD><TD>Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/EM.html" title="class in weka.clusterers">EM</A></B></TD><TD>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.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/FarthestFirst.html" title="class in weka.clusterers">FarthestFirst</A></B></TD><TD>Cluster data using the FarthestFirst algorithm.<br/> <br/> For more information see:<br/> <br/> Hochbaum, Shmoys (1985).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/FilteredClusterer.html" title="class in weka.clusterers">FilteredClusterer</A></B></TD><TD>Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/MakeDensityBasedClusterer.html" title="class in weka.clusterers">MakeDensityBasedClusterer</A></B></TD><TD>Class for wrapping a Clusterer to make it return a distribution and density.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/OPTICS.html" title="class in weka.clusterers">OPTICS</A></B></TD><TD>Mihael Ankerst, Markus M.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/RandomizableClusterer.html" title="class in weka.clusterers">RandomizableClusterer</A></B></TD><TD>Abstract utility class for handling settings common to randomizable clusterers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/RandomizableDensityBasedClusterer.html" title="class in weka.clusterers">RandomizableDensityBasedClusterer</A></B></TD><TD>Abstract utility class for handling settings common to randomizable clusterers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/RandomizableSingleClustererEnhancer.html" title="class in weka.clusterers">RandomizableSingleClustererEnhancer</A></B></TD><TD>Abstract utility class for handling settings common to randomizable clusterers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/SimpleKMeans.html" title="class in weka.clusterers">SimpleKMeans</A></B></TD><TD>Cluster data using the k means algorithm <p/> <!-- globalinfo-end --> <!-- options-start --> Valid options are: <p/></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/SingleClustererEnhancer.html" title="class in weka.clusterers">SingleClustererEnhancer</A></B></TD><TD>Meta-clusterer for enhancing a base clusterer.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../weka/clusterers/XMeans.html" title="class in weka.clusterers">XMeans</A></B></TD><TD>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.</TD></TR></TABLE> <P><DL></DL><HR><!-- ======= START OF BOTTOM NAVBAR ====== --><A NAME="navbar_bottom"><!-- --></A><A HREF="#skip-navbar_bottom" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_bottom_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-tree.html"><FONT CLASS="NavBarFont1"><B>Tree</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" 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