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Handling inconsistency in distributed data mining with paraconsistent logic

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
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 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14154 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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