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A Closer Look at CMAR

  • CMAR (Classification based on Multiple Association Rules: Li, Han, Pei, ICDM’01)
  • Efficiency: Uses an enhanced FP-tree that maintains the distribution of class labels among tuples satisfying each frequent itemset
  • Rule pruning whenever a rule is inserted into the tree
    • Given two rules, R1 and R2, if the antecedent of R1 is more general than that of R2 and conf(R1) ≥ conf(R2), then prune R2
    • Prunes rules for which the rule antecedent and class are not positively correlated, based on a χ2 test of statistical significance
  • Classification based on generated/pruned rules
    • If only one rule satisfies tuple X, assign the class label of the rule
    • If a rule set S satisfies X, CMAR
      • divides S into groups according to class labels
      • uses a weighted χ2 measure to find the strongest group of rules, based on the statistical correlation of rules within a group
      • assigns X the class label of the strongest group

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