Ferreira, Simone N.M. and Freitas, Alex A. and Avila, Braulio C. (2004) Handling inconsistency in distributed data mining with paraconsistent logic. In: Guzelis, C. and Alpaydin, E. and Yakhno, T. and Gurgen, F., eds. Proceedings of the Thirteenth Turkish Symposium on Artificial Intelligence and Neural Networks. , pp. 19-28. ISBN 975-441-213-8. (KAR id:14154)
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Abstract
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.
Item Type: | Book section |
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Uncontrolled keywords: | data mining, paraconsistent logic, classification |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Mark Wheadon |
Date Deposited: | 24 Nov 2008 18:02 UTC |
Last Modified: | 16 Nov 2021 09:52 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/14154 (The current URI for this page, for reference purposes) |
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