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Cobweb (Java Machine Learning Library 0.0.11)
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net.sf.javaml.clustering</FONT>
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Class Cobweb</H2>
<PRE>
java.lang.Object
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><B>net.sf.javaml.clustering.Cobweb</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>Cobweb</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>
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. This algorithm always compares the best host, adding a new leaf, merging the two best hosts, and splitting the best host when considering where to place a new instance.<br/> <br/> For more information see:<br/> <br/> D. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning. 2(2):139-172.<br/> <br/> J. H. Gennari, P. Langley, D. Fisher (1990). Models of incremental concept formation. Artificial Intelligence. 40:11-61. <p/> BibTeX: <pre> @article{Fisher1987, author = {D. Fisher}, journal = {Machine Learning}, number = {2}, pages = {139-172}, title = {Knowledge acquisition via incremental conceptual clustering}, volume = {2}, year = {1987} } @article{Gennari1990, author = {J. H. Gennari and P. Langley and D. Fisher}, journal = {Artificial Intelligence}, pages = {11-61}, title = {Models of incremental concept formation}, volume = {40}, year = {1990} } </pre> <p/>
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<DT><B>Author:</B></DT>
<DD>Mark Hall, Thomas Abeel</DD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/Cobweb.html#Cobweb()">Cobweb</A></B>()</CODE>
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default constructor</TD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/clustering/Cobweb.html#Cobweb(double, double)">Cobweb</A></B>(double acuity,
double cutoff)</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/Cobweb.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="Cobweb()"><!-- --></A><H3>
Cobweb</H3>
<PRE>
public <B>Cobweb</B>()</PRE>
<DL>
<DD>default constructor
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<A NAME="Cobweb(double, double)"><!-- --></A><H3>
Cobweb</H3>
<PRE>
public <B>Cobweb</B>(double acuity,
double cutoff)</PRE>
<DL>
<DD>XXX DOC
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<DT><B>Parameters:</B><DD><CODE>acuity</CODE> - <DD><CODE>cutoff</CODE> - </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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