Abstract
Mining association rules is a well known framework for extracting useful knowledge from databases. They represent a very particular kind of relation, that of co-occurrence between two sets of items. Modifying the usual definition of such rules we may find different kinds of information in the data. Exception rules are examples of rules dealing with unusual knowledge that might be of interest for the user and there exists some approaches for extracting them which employ a set of special association rules.
The goal of this paper is manyfold. First, we provide a deep analysis of the presented previous approaches. We study their advantages, drawbacks and their semantical aspects. Second, we present a new approach using the certainty factor for measuring the strength of exception rules. We also offer a unified formulation for exception rules through the GUHA formal model first presented in the middle sixties by Hájek et al. Third, we define the so called double rules as a new type of rules which in conjunction with exception rules will describe in more detail the relationship between two sets of items. Fourth, we provide an algorithm based on the previous formulation for mining exception and double rules with reasonably good performance and some interesting results.
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Delgado, M., Ruiz, M.D., Sánchez, D. (2010). Mining Exception Rules. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, JL., Yager, R.R. (eds) Foundations of Reasoning under Uncertainty. Studies in Fuzziness and Soft Computing, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10728-3_3
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DOI: https://doi.org/10.1007/978-3-642-10728-3_3
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