Package weka.associations

Class CaRuleGeneration

java.lang.Object
weka.associations.RuleGeneration
weka.associations.CaRuleGeneration
All Implemented Interfaces:
Serializable, RevisionHandler
public class CaRuleGeneration extends RuleGeneration implements Serializable, RevisionHandler
Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules. For association rules in gerneral the method is described in: T. Scheffer (2001). Finding Association Rules That Trade Support Optimally against Confidence. Proc of the 5th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), pp. 424-435. Freiburg, Germany: Springer-Verlag.

The implementation follows the paper expect for adding a rule to the output of the n best rules. A rule is added if: the expected predictive accuracy of this rule is among the n best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).

Version:
$Revision: 1.4 $
Author:
Stefan Mutter (mutter@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • CaRuleGeneration

      public CaRuleGeneration(ItemSet itemSet)
      Constructor
      Parameters:
      itemSet - the item set that forms the premise of the rule

  • Method Details

    • generateRules

      public TreeSet generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances, TreeSet best, int genTime)
      Generates all rules for an item set. The item set is the premise.
      Overrides:
      generateRules in class RuleGeneration
      Parameters:
      numRules - the number of association rules the use wants to mine. This number equals the size n of the list of the best rules.
      midPoints - the mid points of the intervals
      priors - Hashtable that contains the prior probabilities
      expectation - the minimum value of the expected predictive accuracy that is needed to get into the list of the best rules
      instances - the instances for which association rules are generated
      best - the list of the n best rules. The list is implemented as a TreeSet
      genTime - the maximum time of generation
      Returns:
      all the rules with minimum confidence for the given item set

    • aSubsumesB

      public static boolean aSubsumesB(RuleItem a, RuleItem b)
      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.
      Parameters:
      a - an association rule stored as a RuleItem
      b - an association rule stored as a RuleItem
      Returns:
      true if rule a subsumes rule b or false otherwise.

    • singletons

      public static FastVector singletons(Instances instances) throws Exception
      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.
      Parameters:
      instances - the set of instances whose header info is to be used
      Returns:
      a set of item sets, each containing a single item
      Throws:
      Exception - if singletons can't be generated successfully

    • singleConsequence

      public static FastVector singleConsequence(Instances instances)
      generates a consequence of length 1 for a class association rule.
      Parameters:
      instances - the instances under consideration
      Returns:
      FastVector with consequences of length 1

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class RuleGeneration
      Returns:
      the revision