Mining Associative Decision Rules in Decision Tables through Attribute Value Reduction

There are many algorithms and approaches developed to induce decision rules in decision/information tables. Basically, these methods share a common idea: reduction, including row reduction, column reduction, and cell reduction. Most solutions based on the rough set theory integrate these three reductions in the above order, where column reduction is performed by finding attribute reducts and cell reduction is conducted via value reduction. Since there may exist various attribute reducs, many efforts have been put on seeking the best or optimal reduct in the sense of accurate decisions. However, different attribute reducts are only equivalent in the circumstance of the given decision table. The decision rules that are induced from different attribute reducts are not replaceable each other for the coming objects in the future. On the other hand, value reduction is to reduce the decision rules to a logically equivalent minimal subset of minimal length. Traditionally, the value reduct has been searched through the attribute reduct. This method may miss important decision rules. In this paper, a novel method is presented to find associative decision rules in a decision table by value reduction only using the association rule mining technology. Value reduction is conducted in a bottom-up fashion to induce the decision rules without finding any attribute reducts. Our method is described and demonstrated with an illustrative example.