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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Frameset//EN""http://www.w3.org/TR/REC-html40/frameset.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc on Wed Sep 04 10:31:48 CDT 2002 --><TITLE>: Package weka.clusterers</TITLE><LINK REL ="stylesheet" TYPE="text/css" HREF="../../stylesheet.css" TITLE="Style"></HEAD><BODY BGCOLOR="white"><!-- ========== START OF NAVBAR ========== --><A NAME="navbar_top"><!-- --></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0"><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3"> <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 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FRAMES</B></A></FONT></TD></TR></TABLE><!-- =========== END OF NAVBAR =========== --><HR><H2>Package weka.clusterers</H2><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Class Summary</B></FONT></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Clusterer.html">Clusterer</A></B></TD><TD>Abstract clusterer.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="ClusterEvaluation.html">ClusterEvaluation</A></B></TD><TD>Class for evaluating clustering models.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Cobweb.html">Cobweb</A></B></TD><TD>Class implementing the Cobweb and Classit clustering algorithms.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="DistributionClusterer.html">DistributionClusterer</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="DistributionMetaClusterer.html">DistributionMetaClusterer</A></B></TD><TD>Class that wraps up a Clusterer and presents it as a DistributionClusterer for ease of programmatically handling Clusterers in general -- only the one predict method (distributionForInstance) need be worried about.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="EM.html">EM</A></B></TD><TD>Simple EM (estimation maximisation) class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="SimpleKMeans.html">SimpleKMeans</A></B></TD><TD>Simple k means clustering class.</TD></TR></TABLE> <P><HR><!-- ========== START OF NAVBAR ========== --><A NAME="navbar_bottom"><!-- --></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0"><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_bottom_firstrow"><!-- 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