Extracting Compact and Information Lossless Sets of Fuzzy Association Rules

Abstract : Applying classical association rule extraction framework on fuzzy data sets leads to an unmanageably highly sized association rule sets - compounded with an information loss due to the discretization operation - that often constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such a drawback, we advocate the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. This generic basis constitutes a compact nucleus of fuzzy association rules, from which it is possible to informatively derive all the remaining rules. In order to ensure a sound and complete derivation process, we introduce an axiomatic system allowing the complete derivation of all the redundant rules. The results obtained from experiments carried out on benchmark datasets, are very encouraging. They highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.
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Fuzzy Sets and Systems, Elsevier, 2011, 183 (1), pp.1-25. 〈10.1016/j.fss.2011.06.019〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00604420
Contributeur : Sarra Ayouni <>
Soumis le : mercredi 29 juin 2011 - 08:27:13
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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Sarra Ayouni, Sadok Ben Yahia, Anne Laurent. Extracting Compact and Information Lossless Sets of Fuzzy Association Rules. Fuzzy Sets and Systems, Elsevier, 2011, 183 (1), pp.1-25. 〈10.1016/j.fss.2011.06.019〉. 〈lirmm-00604420〉

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