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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

<A NAME="regression(weka.classifiers.m5.Function)"><!-- --></A><H3>
regression</H3>
<PRE>
public final void <B>regression</B>(<A HREF="../../../weka/classifiers/m5/Function.html">Function</A>&nbsp;function)</PRE>
<DL>
<DD>Computes the coefficients of a linear model using the instances at this 
      node<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>function</CODE> - the linear model containing the index of the attributes; 
      coefficients are to be computed</DL>
</DD>
</DL>
<HR>

<A NAME="function()"><!-- --></A><H3>
function</H3>
<PRE>
public final void <B>function</B>()
                    throws java.lang.Exception</PRE>
<DL>
<DD>Finds the appropriate order of the unsmoothed linear model at this node<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="factor(int, int, double)"><!-- --></A><H3>
factor</H3>
<PRE>
public final double <B>factor</B>(int&nbsp;n,
                           int&nbsp;v,
                           double&nbsp;pruningFactor)</PRE>
<DL>
<DD>Calculates a multiplication factor used at this node<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>n</CODE> - the number of instances<DD><CODE>v</CODE> - the number of the coefficients<DT><B>Returns:</B><DD>multiplication factor</DL>
</DD>
</DL>
<HR>

<A NAME="smoothen()"><!-- --></A><H3>
smoothen</H3>
<PRE>
public final void <B>smoothen</B>()</PRE>
<DL>
<DD>Smoothens all unsmoothed formulae at the tree leaves under this node.<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="smoothenFormula(weka.classifiers.m5.Node)"><!-- --></A><H3>
smoothenFormula</H3>
<PRE>
public final void <B>smoothenFormula</B>(<A HREF="../../../weka/classifiers/m5/Node.html">Node</A>&nbsp;current)</PRE>
<DL>
<DD>Recursively smoothens the unsmoothed linear model at this node with the 
     unsmoothed linear models at the nodes above this<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>current</CODE> - the unsmoothed linear model at the up node of the 
     'current' will be used for smoothening</DL>
</DD>
</DL>
<HR>

<A NAME="predictionsToString(weka.core.Instances, int, boolean)"><!-- --></A><H3>
predictionsToString</H3>
<PRE>
public final java.lang.String <B>predictionsToString</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;inst,
                                                  int&nbsp;lmNo,
                                                  boolean&nbsp;smooth)
                                           throws java.lang.Exception</PRE>
<DL>
<DD>Converts the predictions by the tree under this node to a string<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>insta</CODE> - instances<DD><CODE>smooth</CODE> - =ture using the smoothed models; otherwise, the unsmoothed<DT><B>Returns:</B><DD>the converted string<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="leafNum(weka.core.Instance)"><!-- --></A><H3>
leafNum</H3>
<PRE>
public final int <B>leafNum</B>(<A HREF="../../../weka/core/Instance.html">Instance</A>&nbsp;instance)</PRE>
<DL>
<DD>Detects which leaf a instance falls into<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>i</CODE> - instance i<DD><CODE>inst</CODE> - instances<DT><B>Returns:</B><DD>the leaf no.</DL>
</DD>
</DL>
<HR>

<A NAME="predict(weka.core.Instance, boolean)"><!-- --></A><H3>
predict</H3>
<PRE>
public final double <B>predict</B>(<A HREF="../../../weka/core/Instance.html">Instance</A>&nbsp;instance,
                            boolean&nbsp;smooth)</PRE>
<DL>
<DD>Predicts the class value of an instance by the tree<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>i</CODE> - instance i<DT><B>Returns:</B><DD>the predicted value</DL>
</DD>
</DL>
<HR>

<A NAME="errors(weka.core.Instances, boolean)"><!-- --></A><H3>
errors</H3>
<PRE>
public final <A HREF="../../../weka/classifiers/m5/Errors.html">Errors</A> <B>errors</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;inst,
                           boolean&nbsp;smooth)
                    throws java.lang.Exception</PRE>
<DL>
<DD>Evaluates a tree<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - instances<DT><B>Returns:</B><DD>the evaluation results<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="measures(weka.core.Instances, boolean)"><!-- --></A><H3>
measures</H3>
<PRE>
public final <A HREF="../../../weka/classifiers/m5/Measures.html">Measures</A> <B>measures</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;inst,
                               boolean&nbsp;smooth)
                        throws java.lang.Exception</PRE>
<DL>
<DD>Computes performance measures of a tree<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - instances<DD><CODE>smooth</CODE> - =true uses the smoothed models; 
        otherwise uses the unsmoothed models<DT><B>Returns:</B><DD>the performance measures<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="validation(weka.core.Instances)"><!-- --></A><H3>
validation</H3>
<PRE>
public final <A HREF="../../../weka/classifiers/m5/Measures.html">Measures</A>[] <B>validation</B>(<A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;inst)
                            throws java.lang.Exception</PRE>
<DL>
<DD>Computes performance measures for both unsmoothed and smoothed models<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - instances<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="copy(weka.classifiers.m5.Node)"><!-- --></A><H3>
copy</H3>
<PRE>
public final <A HREF="../../../weka/classifiers/m5/Node.html">Node</A> <B>copy</B>(<A HREF="../../../weka/classifiers/m5/Node.html">Node</A>&nbsp;up)
                throws java.lang.Exception</PRE>
<DL>
<DD>Makes a copy of the tree under this node<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>up</CODE> - the parant node of the new node<DT><B>Returns:</B><DD>a copy of the tree under this node<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="measuresToString(weka.classifiers.m5.Measures[], weka.core.Instances, int, int, java.lang.String)"><!-- --></A><H3>
measuresToString</H3>
<PRE>
public final java.lang.String <B>measuresToString</B>(<A HREF="../../../weka/classifiers/m5/Measures.html">Measures</A>[]&nbsp;measures,
                                               <A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;inst,
                                               int&nbsp;lmNo,
                                               int&nbsp;verbosity,
                                               java.lang.String&nbsp;str)
                                        throws java.lang.Exception</PRE>
<DL>
<DD>Converts the performance measures into a string<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>measures[]</CODE> - contains both the unsmoothed and smoothed measures<DD><CODE>inst</CODE> - the instances<DD><CODE>lmNo</CODE> - also converts the predictions by all linear models if lmNo=0, 
        or one linear model spedified by lmNo.<DD><CODE>verbosity</CODE> - the verbosity level<DD><CODE>str</CODE> - the type of evaluation, one of 
        "t" for training, "T" for testing, 
        "f" for fold training, "F" for fold testing, 
        "x" for cross-validation<DT><B>Returns:</B><DD>the converted string<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
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