Yannick Le Bras

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Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support. But candidate set generation is still costly and many rules are uninteresting or redundant. In addition one can miss interesting rules(More)
Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support but candidate set generation is still costly. In addition many rules are uninteresting or redundant and one can miss interesting rules(More)
We propose a formal definition of the robustness of association rules for interestingness measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality measure) might turn out as a very fragile rule with respect to small variations in the(More)
Nous proposons dans cet article une définition formelle de la robus-tesse pour les règles d'association, s'appuyant sur une modélisation que nous avons précédemment définie. Ce concept est à notre avis central dans l'évalua-tion des règles et n'a à ce jour été que très peu étudié de façon satisfaisante. Il est crucial car malgré une très bonne évaluation(More)
—Many studies have shown the limits of sup-port/confidence framework used in Apriori-like algorithms to mine association rules. One solution to cope with this limitation is to get rid of frequent itemset mining and to focus as soon as possible on interesting rules. Many works have focused on the algorithmic properties of the confidence. In particular, the(More)
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