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A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules

Abstract : In this paper, we tackled the problem of generation of rare classification rules. Our work is motivated by the search of an effective algorithm allowing the extraction of rare classification rules by avoiding the generation of a large number of patterns at reduced time. Within this framework we are interested in rules of the form a 1 ∧ a 2... ∧ a n ⇒b which allow us to propose a new approach based on genetic algorithms principle. This approach allows obtaining frequent and rare rules while avoiding making a breadth search. We describe our method and provide a comparative study of three versions of our method on standard benchmark data sets
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00757482
Contributor : Michel Liquiere <>
Submitted on : Tuesday, November 27, 2012 - 10:05:26 AM
Last modification on : Thursday, May 24, 2018 - 3:59:23 PM

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  • HAL Id : lirmm-00757482, version 1

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Ines Bouzouita, Michel Liquière, Samir Elloumi, Ali Jaoua. A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules. ISA: Information Security and Assurance, Aug 2011, Brno University, Czech Republic. pp.43-52. ⟨lirmm-00757482⟩

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