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<DD>Constructor
<P>
<DT><B>Parameters:</B><DD><CODE>totalTrans</CODE> - the total number of transactions in the data</DL>

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<B>Method Detail</B></FONT></TD>
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<A NAME="confidenceForRule(weka.associations.AprioriItemSet, weka.associations.AprioriItemSet)"><!-- --></A><H3>
confidenceForRule</H3>
<PRE>
public static double <B>confidenceForRule</B>(<A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;premise,
                                       <A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;consequence)</PRE>
<DL>
<DD>Outputs the confidence for a rule.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>premise</CODE> - the premise of the rule<DD><CODE>consequence</CODE> - the consequence of the rule
<DT><B>Returns:</B><DD>the confidence on the training data</DL>
</DD>
</DL>
<HR>

<A NAME="liftForRule(weka.associations.AprioriItemSet, weka.associations.AprioriItemSet, int)"><!-- --></A><H3>
liftForRule</H3>
<PRE>
public double <B>liftForRule</B>(<A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;premise,
                          <A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;consequence,
                          int&nbsp;consequenceCount)</PRE>
<DL>
<DD>Outputs the lift for a rule. Lift is defined as:<br>
 confidence / prob(consequence)
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>premise</CODE> - the premise of the rule<DD><CODE>consequence</CODE> - the consequence of the rule<DD><CODE>consequenceCount</CODE> - how many times the consequence occurs independent
 of the premise
<DT><B>Returns:</B><DD>the lift on the training data</DL>
</DD>
</DL>
<HR>

<A NAME="leverageForRule(weka.associations.AprioriItemSet, weka.associations.AprioriItemSet, int, int)"><!-- --></A><H3>
leverageForRule</H3>
<PRE>
public double <B>leverageForRule</B>(<A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;premise,
                              <A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;consequence,
                              int&nbsp;premiseCount,
                              int&nbsp;consequenceCount)</PRE>
<DL>
<DD>Outputs the leverage for a rule. Leverage is defined as: <br>
 prob(premise & consequence) - (prob(premise) * prob(consequence))
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>premise</CODE> - the premise of the rule<DD><CODE>consequence</CODE> - the consequence of the rule<DD><CODE>premiseCount</CODE> - how many times the premise occurs independent
 of the consequent<DD><CODE>consequenceCount</CODE> - how many times the consequence occurs independent
 of the premise
<DT><B>Returns:</B><DD>the leverage on the training data</DL>
</DD>
</DL>
<HR>

<A NAME="convictionForRule(weka.associations.AprioriItemSet, weka.associations.AprioriItemSet, int, int)"><!-- --></A><H3>
convictionForRule</H3>
<PRE>
public double <B>convictionForRule</B>(<A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;premise,
                                <A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;consequence,
                                int&nbsp;premiseCount,
                                int&nbsp;consequenceCount)</PRE>
<DL>
<DD>Outputs the conviction for a rule. Conviction is defined as: <br>
 prob(premise) * prob(!consequence) / prob(premise & !consequence)
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>premise</CODE> - the premise of the rule<DD><CODE>consequence</CODE> - the consequence of the rule<DD><CODE>premiseCount</CODE> - how many times the premise occurs independent
 of the consequent<DD><CODE>consequenceCount</CODE> - how many times the consequence occurs independent
 of the premise
<DT><B>Returns:</B><DD>the conviction on the training data</DL>
</DD>
</DL>
<HR>

<A NAME="generateRules(double, weka.core.FastVector, int)"><!-- --></A><H3>
generateRules</H3>
<PRE>
public <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>[] <B>generateRules</B>(double&nbsp;minConfidence,
                                  <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>&nbsp;hashtables,
                                  int&nbsp;numItemsInSet)</PRE>
<DL>
<DD>Generates all rules for an item set.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>minConfidence</CODE> - the minimum confidence the rules have to have<DD><CODE>hashtables</CODE> - containing all(!) previously generated
 item sets<DD><CODE>numItemsInSet</CODE> - the size of the item set for which the rules
 are to be generated
<DT><B>Returns:</B><DD>all the rules with minimum confidence for the given item set</DL>
</DD>
</DL>
<HR>

<A NAME="generateRulesBruteForce(double, int, weka.core.FastVector, int, int, double)"><!-- --></A><H3>
generateRulesBruteForce</H3>
<PRE>
public final <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>[] <B>generateRulesBruteForce</B>(double&nbsp;minMetric,
                                                  int&nbsp;metricType,
                                                  <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>&nbsp;hashtables,
                                                  int&nbsp;numItemsInSet,
                                                  int&nbsp;numTransactions,
                                                  double&nbsp;significanceLevel)
                                           throws java.lang.Exception</PRE>
<DL>
<DD>Generates all significant rules for an item set.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>minMetric</CODE> - the minimum metric (confidence, lift, leverage, 
 improvement) the rules have to have<DD><CODE>metricType</CODE> - (confidence=0, lift, leverage, improvement)<DD><CODE>hashtables</CODE> - containing all(!) previously generated
 item sets<DD><CODE>numItemsInSet</CODE> - the size of the item set for which the rules
 are to be generated
<DT><B>Returns:</B><DD>all the rules with minimum metric for the given item set
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="subtract(weka.associations.AprioriItemSet)"><!-- --></A><H3>
subtract</H3>
<PRE>
public final <A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A> <B>subtract</B>(<A HREF="../../weka/associations/AprioriItemSet.html" title="class in weka.associations">AprioriItemSet</A>&nbsp;toSubtract)</PRE>
<DL>
<DD>Subtracts an item set from another one.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>toSubtract</CODE> - the item set to be subtracted from this one.
<DT><B>Returns:</B><DD>an item set that only contains items form this item sets that
 are not contained by toSubtract</DL>
</DD>
</DL>
<HR>

<A NAME="toString(weka.core.Instances)"><!-- --></A><H3>
toString</H3>
<PRE>
public final java.lang.String <B>toString</B>(<A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances)</PRE>
<DL>
<DD>Returns the contents of an item set as a string.
<P>
<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/associations/ItemSet.html#toString(weka.core.Instances)">toString</A></CODE> in class <CODE><A HREF="../../weka/associations/ItemSet.html" title="class in weka.associations">ItemSet</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - contains the relevant header information
<DT><B>Returns:</B><DD>string describing the item set</DL>
</DD>
</DL>
<HR>

<A NAME="singletons(weka.core.Instances)"><!-- --></A><H3>
singletons</H3>
<PRE>
public static <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> <B>singletons</B>(<A HREF="../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances)
                             throws java.lang.Exception</PRE>
<DL>
<DD>Converts the header info of the given set of instances into a set 
 of item sets (singletons). The ordering of values in the header file 
 determines the lexicographic order.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - the set of instances whose header info is to be used
<DT><B>Returns:</B><DD>a set of item sets, each containing a single item
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if singletons can't be generated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="mergeAllItemSets(weka.core.FastVector, int, int)"><!-- --></A><H3>
mergeAllItemSets</H3>
<PRE>
public static <A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> <B>mergeAllItemSets</B>(<A HREF="../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>&nbsp;itemSets,
                                          int&nbsp;size,
                                          int&nbsp;totalTrans)</PRE>
<DL>
<DD>Merges all item sets in the set of (k-1)-item sets 
 to create the (k)-item sets and updates the counters.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>itemSets</CODE> - the set of (k-1)-item sets<DD><CODE>size</CODE> - the value of (k-1)
<DT><B>Returns:</B><DD>the generated (k)-item sets</DL>
</DD>
</DL>
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