Handling inconsistency in distributed data mining with paraconsistent logic

This paper addresses the problem of inconsistent rule subsets in distributed data mining. In this scenario, N rule subsets are independently discovered from N different data subsets. This can result in inconsistent rules – i.e. rules with the same antecedent but different class predictions – across the N rule subsets. In order to handle these rule inconsistencies, this paper proposes a paraconsistent logic-based method for post-processing different rule subsets discovered by a rule induction algorithm in a distributed data mining scenario. The proposed method produces a global inconsistency-free rule set by using principles and concepts of paraconsistent logic, a relatively novel kind of logic developed specifically for inconsistency handling.