rulegeneration.html

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<A NAME="RuleGeneration(weka.associations.ItemSet)"><!-- --></A><H3>
RuleGeneration</H3>
<PRE>
public <B>RuleGeneration</B>(<A HREF="../../weka/associations/ItemSet.html" title="class in weka.associations">ItemSet</A>&nbsp;itemSet)</PRE>
<DL>
<DD>Constructor
<P>
<DT><B>Parameters:</B><DD><CODE>itemSet</CODE> - item set for that rules should be generated.
 The item set will form the premise of the rules.</DL>

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<A NAME="binomialDistribution(double, double, double)"><!-- --></A><H3>
binomialDistribution</H3>
<PRE>
public static final double <B>binomialDistribution</B>(double&nbsp;accuracy,
                                                double&nbsp;ruleCount,
                                                double&nbsp;premiseCount)</PRE>
<DL>
<DD>calculates the probability using a binomial distribution.
 If the support of the premise is too large this distribution
 is approximated by a normal distribution.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>accuracy</CODE> - the accuracy value<DD><CODE>ruleCount</CODE> - the support of the whole rule<DD><CODE>premiseCount</CODE> - the support of the premise
<DT><B>Returns:</B><DD>the probability value</DL>
</DD>
</DL>
<HR>

<A NAME="expectation(double, int, double[], java.util.Hashtable)"><!-- --></A><H3>
expectation</H3>
<PRE>
public static final double <B>expectation</B>(double&nbsp;ruleCount,
                                       int&nbsp;premiseCount,
                                       double[]&nbsp;midPoints,
                                       java.util.Hashtable&nbsp;priors)</PRE>
<DL>
<DD>calculates the expected predctive accuracy of a rule
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>ruleCount</CODE> - the support of the rule<DD><CODE>premiseCount</CODE> - the premise support of the rule<DD><CODE>midPoints</CODE> - array with all mid points<DD><CODE>priors</CODE> - hashtable containing the prior probabilities
<DT><B>Returns:</B><DD>the expected predictive accuracy</DL>
</DD>
</DL>
<HR>

<A NAME="generateRules(int, double[], java.util.Hashtable, double, weka.core.Instances, java.util.TreeSet, int)"><!-- --></A><H3>
generateRules</H3>
<PRE>
public java.util.TreeSet <B>generateRules</B>(int&nbsp;numRules,
                                       double[]&nbsp;midPoints,
                                       java.util.Hashtable&nbsp;priors,
                                       double&nbsp;expectation,
                                       <A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                                       java.util.TreeSet&nbsp;best,
                                       int&nbsp;genTime)</PRE>
<DL>
<DD>Generates all rules for an item set. The item set is the premise.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>numRules</CODE> - the number of association rules the use wants to mine.
 This number equals the size <i>n<\i> of the list of the
 best rules.<DD><CODE>midPoints</CODE> - the mid points of the intervals<DD><CODE>priors</CODE> - Hashtable that contains the prior probabilities<DD><CODE>expectation</CODE> - the minimum value of the expected predictive accuracy
 that is needed to get into the list of the best rules<DD><CODE>instances</CODE> - the instances for which association rules are generated<DD><CODE>best</CODE> - the list of the <i>n<\i> best rules.
 The list is implemented as a TreeSet<DD><CODE>genTime</CODE> - the maximum time of generation
<DT><B>Returns:</B><DD>all the rules with minimum confidence for the given item set</DL>
</DD>
</DL>
<HR>

<A NAME="aSubsumesB(weka.associations.RuleItem, weka.associations.RuleItem)"><!-- --></A><H3>
aSubsumesB</H3>
<PRE>
public static boolean <B>aSubsumesB</B>(<A HREF="../../weka/associations/RuleItem.html" title="class in weka.associations">RuleItem</A>&nbsp;a,
                                 <A HREF="../../weka/associations/RuleItem.html" title="class in weka.associations">RuleItem</A>&nbsp;b)</PRE>
<DL>
<DD>Methods that decides whether or not rule a subsumes rule b.
 The defintion of subsumption is:
 Rule a subsumes rule b, if a subsumes b
 AND
 a has got least the same expected predictive accuracy as b.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>a</CODE> - an association rule stored as a RuleItem<DD><CODE>b</CODE> - an association rule stored as a RuleItem
<DT><B>Returns:</B><DD>true if rule a subsumes rule b or false otherwise.</DL>
</DD>
</DL>
<HR>

<A NAME="singleConsequence(weka.core.Instances, int, weka.core.FastVector)"><!-- --></A><H3>
singleConsequence</H3>
<PRE>
public static <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> <B>singleConsequence</B>(<A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                                           int&nbsp;attNum,
                                           <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>&nbsp;consequences)</PRE>
<DL>
<DD>generates a consequence of length 1 for an association rule.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - the instances under consideration<DD><CODE>attNum</CODE> - an item that does not occur in the premise<DD><CODE>consequences</CODE> - FastVector that possibly already contains other consequences of length 1
<DT><B>Returns:</B><DD>FastVector with consequences of length 1</DL>
</DD>
</DL>
<HR>

<A NAME="removeRedundant(weka.associations.RuleItem)"><!-- --></A><H3>
removeRedundant</H3>
<PRE>
public boolean <B>removeRedundant</B>(<A HREF="../../weka/associations/RuleItem.html" title="class in weka.associations">RuleItem</A>&nbsp;toInsert)</PRE>
<DL>
<DD>Method that removes redundant rules out of the list of the best rules.
 A rule is in that list if:
 the expected predictive accuracy of this rule is among the best and it is
 not subsumed by a rule with at least the same expected predictive accuracy
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>toInsert</CODE> - the rule that should be inserted into the list
<DT><B>Returns:</B><DD>true if the method has changed the list, false otherwise</DL>
</DD>
</DL>
<HR>

<A NAME="count()"><!-- --></A><H3>
count</H3>
<PRE>
public int <B>count</B>()</PRE>
<DL>
<DD>Gets the actual maximum value of the generation time
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the actual maximum value of the generation time</DL>
</DD>
</DL>
<HR>

<A NAME="change()"><!-- --></A><H3>
change</H3>
<PRE>
public boolean <B>change</B>()</PRE>
<DL>
<DD>Gets if the list fo the best rules has been changed
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>whether or not the list fo the best rules has been changed</DL>
</DD>
</DL>
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