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< 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

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<TD><B>Methods inherited from class java.lang.Object</B></TD>
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<TD><CODE>clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait</CODE></TD>
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<A NAME="m_Distributions"><!-- --></A><H3>
m_Distributions</H3>
<PRE>
protected <A HREF="../../weka/estimators/Estimator.html">Estimator</A>[][] <B>m_Distributions</B></PRE>
<DL>
<DD>The attribute estimators.</DL>
<HR>

<A NAME="m_ClassDistribution"><!-- --></A><H3>
m_ClassDistribution</H3>
<PRE>
protected <A HREF="../../weka/estimators/Estimator.html">Estimator</A> <B>m_ClassDistribution</B></PRE>
<DL>
<DD>The class estimator.</DL>
<HR>

<A NAME="m_UseKernelEstimator"><!-- --></A><H3>
m_UseKernelEstimator</H3>
<PRE>
protected boolean <B>m_UseKernelEstimator</B></PRE>
<DL>
<DD>Whether to use kernel density estimator rather than normal distribution
 for numeric attributes</DL>
<HR>

<A NAME="m_NumClasses"><!-- --></A><H3>
m_NumClasses</H3>
<PRE>
protected int <B>m_NumClasses</B></PRE>
<DL>
<DD>The number of classes (or 1 for numeric class)</DL>
<HR>

<A NAME="m_Instances"><!-- --></A><H3>
m_Instances</H3>
<PRE>
protected <A HREF="../../weka/core/Instances.html">Instances</A> <B>m_Instances</B></PRE>
<DL>
<DD>The dataset header for the purposes of printing out a semi-intelligible 
 model</DL>
<HR>

<A NAME="DEFAULT_NUM_PRECISION"><!-- --></A><H3>
DEFAULT_NUM_PRECISION</H3>
<PRE>
protected static final double <B>DEFAULT_NUM_PRECISION</B></PRE>
<DL>
<DD>The precision parameter used for numeric attributes</DL>

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<A NAME="NaiveBayes()"><!-- --></A><H3>
NaiveBayes</H3>
<PRE>
public <B>NaiveBayes</B>()</PRE>
<DL>
</DL>

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<A NAME="buildClassifier(weka.core.Instances)"><!-- --></A><H3>
buildClassifier</H3>
<PRE>
public void <B>buildClassifier</B>(<A HREF="../../weka/core/Instances.html">Instances</A>&nbsp;instances)
                     throws java.lang.Exception</PRE>
<DL>
<DD>Generates the classifier.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/Classifier.html#buildClassifier(weka.core.Instances)">buildClassifier</A></CODE> in class <CODE><A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - set of instances serving as training data<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the classifier has not been generated 
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="updateClassifier(weka.core.Instance)"><!-- --></A><H3>
updateClassifier</H3>
<PRE>
public void <B>updateClassifier</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;instance)
                      throws java.lang.Exception</PRE>
<DL>
<DD>Updates the classifier with the given instance.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instance</CODE> - the new training instance to include in the model<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the instance could not be incorporated in
 the model.</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;instance)
                                 throws java.lang.Exception</PRE>
<DL>
<DD>Calculates the class membership probabilities for the given test 
 instance.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/DistributionClassifier.html#distributionForInstance(weka.core.Instance)">distributionForInstance</A></CODE> in class <CODE><A HREF="../../weka/classifiers/DistributionClassifier.html">DistributionClassifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instance</CODE> - the instance to be classified<DT><B>Returns:</B><DD>predicted class probability distribution<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if there is a problem generating the prediction</DL>
</DD>
</DL>
<HR>

<A NAME="listOptions()"><!-- --></A><H3>
listOptions</H3>
<PRE>
public java.util.Enumeration <B>listOptions</B>()</PRE>
<DL>
<DD>Returns an enumeration describing the available options<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#listOptions()">listOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>an enumeration of all the available options</DL>
</DD>
</DL>
<HR>

<A NAME="setOptions(java.lang.String[])"><!-- --></A><H3>
setOptions</H3>
<PRE>
public void <B>setOptions</B>(java.lang.String[]&nbsp;options)
                throws java.lang.Exception</PRE>
<DL>
<DD>Parses a given list of options. Valid options are:<p>

 -K <br>
 Use kernel estimation for modelling numeric attributes rather than
 a single normal distribution.<p><DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#setOptions(java.lang.String[])">setOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>options</CODE> - the list of options as an array of strings<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if an option is not supported</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 the classifier.<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="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns a description of the classifier.<DD><DL>
<DT><B>Overrides:</B><DD><CODE>toString</CODE> in class <CODE>java.lang.Object</CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>a description of the classifier as a string.</DL>
</DD>
</DL>
<HR>

<A NAME="getUseKernelEstimator()"><!-- --></A><H3>
getUseKernelEstimator</H3>
<PRE>
public boolean <B>getUseKernelEstimator</B>()</PRE>
<DL>
<DD>Gets if kernel estimator is being used.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>Value of m_UseKernelEstimatory.</DL>
</DD>
</DL>
<HR>

<A NAME="setUseKernelEstimator(boolean)"><!-- --></A><H3>
setUseKernelEstimator</H3>
<PRE>
public void <B>setUseKernelEstimator</B>(boolean&nbsp;v)</PRE>
<DL>
<DD>Sets if kernel estimator is to be used.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>v</CODE> - Value to assign to m_UseKernelEstimatory.</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> - the options</DL>
</DD>
</DL>
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