Abstract
Mining gradual rules of the form −“the more A, the more B”− is more and more grasping the interest of the data mining community. Several approaches have been recently proposed. Unfortunately, in all surveyed approaches, reducing the quantity of mined patterns (and, consequently, the quantity of extracted rules) was not the main concern. To palliate such a drawback, a possible solution consists in using results of Formal Concept Analysis to generate a lossless reduced size nucleus of gradual patterns. To do so, we introduce in this paper a novel closure operator acting on gradual itemsets. Results of the experiments carried out on synthetic datasets showed important profits in terms of compactness of the generated gradual patterns set.
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Ayouni, S., Laurent, A., Yahia, S.B., Poncelet, P. (2010). Mining Closed Gradual Patterns. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_34
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DOI: https://doi.org/10.1007/978-3-642-13208-7_34
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