Association Action Rules

TLDR

Action rules model state transitions of objects relative to a key attribute, yet prior work typically required first extracting classification rules before generating action rules. This study introduces a new method for generating association‑type action rules, defining representative and simple association action rules and presenting algorithms to construct them directly from frequent action sets with minimal cost. The method employs frequent action sets and an Apriori‑like strategy to generate them, then uses algorithms to construct representative and simple association action rules directly from these sets.

Abstract

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Previous research on action rule discovery usually required the extraction of classification rules before constructing any action rule. This paper gives anew approach for generating association-type action rules. The notion of frequent action sets and Apriori-like strategy generating them is proposed. We introduce the notion of a representative action rules and give an algorithm to construct them directly from frequent action sets. Finally, we introduce the notion of a simple association action rule, the cost of association action rule, and give a strategy to construct simple association action rules of a lowest cost.