em.html

< 数据挖掘--实用机器学习技术及java实现> 一书结合数据挖掘和机器学习的知识,作者陈述了自动挖掘模式的基础理论,并且以java语言实现了具有代表性的各类数据挖掘方法.例如:class

 displaying in the explorer/experimenter gui</DL>
</DD>
</DL>
<HR>

<A NAME="setNumClusters(int)"><!-- --></A><H3>
setNumClusters</H3>
<PRE>
public void <B>setNumClusters</B>(int&nbsp;n)
                    throws java.lang.Exception</PRE>
<DL>
<DD>Set the number of clusters (-1 to select by CV).<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>n</CODE> - the number of clusters<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if n is 0</DL>
</DD>
</DL>
<HR>

<A NAME="getNumClusters()"><!-- --></A><H3>
getNumClusters</H3>
<PRE>
public int <B>getNumClusters</B>()</PRE>
<DL>
<DD>Get the number of clusters<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the number of clusters.</DL>
</DD>
</DL>
<HR>

<A NAME="maxIterationsTipText()"><!-- --></A><H3>
maxIterationsTipText</H3>
<PRE>
public java.lang.String <B>maxIterationsTipText</B>()</PRE>
<DL>
<DD>Returns the tip text for this property<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>tip text for this property suitable for
 displaying in the explorer/experimenter gui</DL>
</DD>
</DL>
<HR>

<A NAME="setMaxIterations(int)"><!-- --></A><H3>
setMaxIterations</H3>
<PRE>
public void <B>setMaxIterations</B>(int&nbsp;i)
                      throws java.lang.Exception</PRE>
<DL>
<DD>Set the maximum number of iterations to perform<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>i</CODE> - the number of iterations<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if i is less than 1</DL>
</DD>
</DL>
<HR>

<A NAME="getMaxIterations()"><!-- --></A><H3>
getMaxIterations</H3>
<PRE>
public int <B>getMaxIterations</B>()</PRE>
<DL>
<DD>Get the maximum number of iterations<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the number of iterations</DL>
</DD>
</DL>
<HR>

<A NAME="setDebug(boolean)"><!-- --></A><H3>
setDebug</H3>
<PRE>
public void <B>setDebug</B>(boolean&nbsp;v)</PRE>
<DL>
<DD>Set debug mode - verbose output<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>v</CODE> - true for verbose output</DL>
</DD>
</DL>
<HR>

<A NAME="getDebug()"><!-- --></A><H3>
getDebug</H3>
<PRE>
public boolean <B>getDebug</B>()</PRE>
<DL>
<DD>Get debug mode<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>true if debug mode is set</DL>
</DD>
</DL>
<HR>

<A NAME="getOptions()"><!-- --></A><H3>
getOptions</H3>
<PRE>
public java.lang.String[] <B>getOptions</B>()</PRE>
<DL>
<DD>Gets the current settings of EM.<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#getOptions()">getOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>an array of strings suitable for passing to setOptions()</DL>
</DD>
</DL>
<HR>

<A NAME="resetOptions()"><!-- --></A><H3>
resetOptions</H3>
<PRE>
protected void <B>resetOptions</B>()</PRE>
<DL>
<DD>Reset to default options<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Outputs the generated clusters into a string.<DD><DL>
<DT><B>Overrides:</B><DD><CODE>toString</CODE> in class <CODE>java.lang.Object</CODE></DL>
</DD>
</DL>
<HR>

<A NAME="numberOfClusters()"><!-- --></A><H3>
numberOfClusters</H3>
<PRE>
public int <B>numberOfClusters</B>()
                     throws java.lang.Exception</PRE>
<DL>
<DD>Returns the number of clusters.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/clusterers/Clusterer.html#numberOfClusters()">numberOfClusters</A></CODE> in class <CODE><A HREF="../../weka/clusterers/Clusterer.html">Clusterer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the number of clusters generated for a training dataset.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if number of clusters could not be returned
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="buildClusterer(weka.core.Instances)"><!-- --></A><H3>
buildClusterer</H3>
<PRE>
public void <B>buildClusterer</B>(<A HREF="../../weka/core/Instances.html">Instances</A>&nbsp;data)
                    throws java.lang.Exception</PRE>
<DL>
<DD>Generates a clusterer. Has to initialize all fields of the clusterer
 that are not being set via options.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/clusterers/Clusterer.html#buildClusterer(weka.core.Instances)">buildClusterer</A></CODE> in class <CODE><A HREF="../../weka/clusterers/Clusterer.html">Clusterer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - set of instances serving as training data<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the clusterer has not been 
 generated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="densityForInstance(weka.core.Instance)"><!-- --></A><H3>
densityForInstance</H3>
<PRE>
public double <B>densityForInstance</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;inst)
                          throws java.lang.Exception</PRE>
<DL>
<DD>Computes the density for a given instance.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/clusterers/DistributionClusterer.html#densityForInstance(weka.core.Instance)">densityForInstance</A></CODE> in class <CODE><A HREF="../../weka/clusterers/DistributionClusterer.html">DistributionClusterer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - the instance to compute the density for<DT><B>Returns:</B><DD>the density.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the density could not be computed
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="distributionForInstance(weka.core.Instance)"><!-- --></A><H3>
distributionForInstance</H3>
<PRE>
public double[] <B>distributionForInstance</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;inst)
                                 throws java.lang.Exception</PRE>
<DL>
<DD>Predicts the cluster memberships for a given instance.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/clusterers/DistributionClusterer.html#distributionForInstance(weka.core.Instance)">distributionForInstance</A></CODE> in class <CODE><A HREF="../../weka/clusterers/DistributionClusterer.html">DistributionClusterer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - set of test instances<DD><CODE>instance</CODE> - the instance to be assigned a cluster.<DT><B>Returns:</B><DD>an array containing the estimated membership 
 probabilities of the test instance in each cluster (this 
 should sum to at most 1)<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if distribution could not be 
 computed successfully</DL>
</DD>
</DL>
<HR>

<A NAME="weightsForInstance(weka.core.Instance)"><!-- --></A><H3>
weightsForInstance</H3>
<PRE>
protected double[] <B>weightsForInstance</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;inst)
                               throws java.lang.Exception</PRE>
<DL>
<DD>Returns the weights (indicating cluster membership) for a given instance<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - the instance to be assigned a cluster<DT><B>Returns:</B><DD>an array of weights<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if weights could not be computed</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;argv)</PRE>
<DL>
<DD>Main method for testing this class.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>argv</CODE> - should contain the following arguments: <p>
 -t training file [-T test file] [-N number of clusters] [-S random seed]</DL>
</DD>
</DL>
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