MDPREF: a Mining Algorithm for Filtering and Selecting Relevant Association Rules

Mohammed Mouhir; Youssef Balouki; Taoufiq Gadi

doi:10.15866/irecos.v11i11.10141

MDPREF: a Mining Algorithm for Filtering and Selecting Relevant Association Rules

Mohammed Mouhir^(1*), Youssef Balouki⁽²⁾, Taoufiq Gadi⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v11i11.10141

Abstract

Decision Support System designs the means and instruments together with methods aimed at getting through a series of operations with the purpose of satisfying the needs of the decision-makers. Most of the algorithms devoted to this purpose and especially to mining relevant association rules generate a large number of rules suffering from a problem of choosing a threshold, a problem of preferring some measures to others. This paper proposes a novel approach, named "MDPREF" (Most Dominant and Preferential) Rules, that serves to filter, to prune and to discover interesting rules by assuming simultaneously the notion of dominance between rules on the one hand and the user-preference on the other; going hand in hand with this approach is an auxiliary algorithm, Classifying Rules, whose task is to rank and classify rules. In order to evaluate the efficiency of the main algorithm, we use a real database, and then we compare our results with the ones issued from other algorithms.

Keywords

Association Rules Mining; Undominate Rules; Preference Rules; User Profile Mining; MDPREF Algorithm

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