Generating a Condensed Representation for Association Rules |
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Authors: | Email author" target="_blank">Nicolas?PasquierEmail author Rafik?Taouil Yves?Bastide Gerd?Stumme Lotfi?Lakhal |
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Affiliation: | (1) I3S (CNRS UMR 6070)—Université de Nice-Sophia Antipolis, 06903, Sophia Antipolis, France;(2) LI—Université Francois Rabelais de Tours, 3 place Jean Jaurès, 41000 Blois, France;(3) IRISA—INRIA Rennes, Campus Universitaire de Beaulieu, 35042 Rennes, France;(4) Fachbereich Mathematik/Informatik, Universität Kassel, 34121 Kassel, Germany;(5) LIM (CNRS FRE 2246)—Université de la Méditerranée, case 901, 13288 Marseille, France |
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Abstract: | Association rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules. Moreover, many of these rules are redundant and thus useless. Using a semantic based on the closure of the Galois connection, we define a condensed representation for association rules. This representation is characterized by frequent closed itemsets and their generators. It contains the non-redundant association rules having minimal antecedent and maximal consequent, called min-max association rules. We think that these rules are the most relevant since they are the most general non-redundant association rules. Furthermore, this representation is a basis, i.e., a generating set for all association rules, their supports and their confidences, and all of them can be retrieved needless accessing the data. We introduce algorithms for extracting this basis and for reconstructing all association rules. Results of experiments carried out on real datasets show the usefulness of this approach. In order to generate this basis when an algorithm for extracting frequent itemsets—such as Apriori for instance—is used, we also present an algorithm for deriving frequent closed itemsets and their generators from frequent itemsets without using the dataset. |
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Keywords: | data mining Galois closure operator frequent closed itemsets generators min-max association rules basis for association rules condensed representation |
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