Handling inconsistency in distributed data mining with paraconsistent logic

Ferreira, S.N.M and Freitas, A.A. and Avila, B.C. (2004) 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)

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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: Conference or workshop item (Paper)
Uncontrolled keywords: data mining, paraconsistent logic, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02
Last Modified: 09 Apr 2014 14:55
Resource URI: http://kar.kent.ac.uk/id/eprint/14154 (The current URI for this page, for reference purposes)
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