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Extraction de règles d'association pour la prédiction de valeurs manquantes

Abstract : Missing values in databases have motivated many researches in the field of KDD, specially concerning prediction. However, to the best of our knowledge, few appraoches based on association rules have been proposed so far. In this paper, we show how to adapt the levelwise algorithm for the mining of association rules in order to mine frequent rules with a confidence equal to 1 from a relational table. In our approach, the consequents of extracted rules are either an interval or a set of values, according to whether the domain of the predicted attribute is continuous or discrete.
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Sylvie Jami, Tao-Yan Jen, Dominique Laurent, Georges Loizou, Oumar Sy. Extraction de règles d'association pour la prédiction de valeurs manquantes. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2005, Volume 3, Special Issue CARI'04, november 2005, pp.103-124. ⟨10.46298/arima.1834⟩. ⟨hal-01261706⟩



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