Traversing Itemset Lattices with Statistical Metric Pruning

ABSTRACT We study how to e ciently compute signi cant association rules according to common statistical measures such as a chi-squared value or correlation coe cient. For this purpose, one might consider to use of the Apriori algorithm, but the algorithm needs major conversion, because none of these statistical metrics are anti-monotone, and the use of higher support for reducing the search space cannot guarantee solutions in its the search space. We here present a method of estimating a tight upper bound on the statistical metric associated with any superset of an itemset, as well as the novel use of the resulting information of upper bounds to prune unproductive supersets while traversing itemset lattices. Experimental tests demonstrate the e ciency of this method.