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: 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: 13 Jun 2014 12:58
Resource URI: http://kar.kent.ac.uk/id/eprint/14154 (The current URI for this page, for reference purposes)
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