BruteSuppression: a size reduction method for Apriori rule sets
Association rule mining can provide genuine insight into the data being analysed; however, rule sets can be extremely large, and therefore difficult and time-consuming for the user to interpret. We propose reducing the size of Apriori rule sets by removing overlapping rules, and compare this approach with two standard methods for reducing rule set size: increasing the minimum confidence parameter, and increasing the minimum antecedent support parameter. We evaluate the rule sets in terms of confidence and coverage, as well as two rule interestingness measures that favour rules with antecedent conditions that are poor individual predictors of the target class, as we assume that these represent potentially interesting rules. We also examine the distribution of the rules graphically, to assess whether particular classes of rules are eliminated. We show that removing overlapping rules substantially reduces rule set size in most cases, and alters the character of a rule set less than if the standard parameters are used to constrain the rule set to the same size. Based on our results, we aim to extend the Apriori algorithm to incorporate the suppression of overlapping rules.
apriori rules interestingness data mining partial classification