Apriori Modified for Action Rules Mining
Lukáš Sýkora, Tomáš Kliegr · 2023
Action rule mining is an extension of classification rule learning, in which an action rule provides information similar to that of a standard classification rule and also suggests a course of action. Action rules are used to obtain counterfactual explanations. Rule-based action rule mining involves two separate steps: mining classification rules, typically using the Apriori algorithm, and a user-set minimum support and confidence threshold. The second step involves forming action rules from the classification rules by utilizing a second set of user-set parameters including the desired and undesired values of the target attribute, a list of stable attributes, and a list of flexible attributes. This two-stage approach is inefficient as the first step tends to generate excessively many classification rules, most of which can never form an action rule meeting the second set of parameters. This paper describes a modified Apriori algorithm for action rule mining (Action-Apriori), which includes an enhanced version of downward closure that further reduces the space of possible candidates by checking whether the candidate itemsets also meet the second set of user-set parameters. The benchmarks show a consistent reduction in the learning time of the new algorithm compared to the state-of-the-art ARAS algorithm. This paper is supplemented by an open-source implementation.