Ferreira, S.N.M and Freitas, A.A. and Avila, B.C.
Handling inconsistency in distributed data mining with paraconsistent logic.
In: Proc. 13th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN-2004).
(Full text available)
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.
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