Abstract
In association rules mining, current trend is witnessing the emergence of a growing number of works toward bringing negative items to light in the mined knowledge. However, the amount of the extracted rules is huge, thus not feasible in practice. In this paper, we propose to extract a subset of generalized association rules (i.e., association rules with negation) from which we can retrieve the whole set of generalized association rules. Results of experiments carried out on benchmark databases showed important profits in terms of compactness of the introduced generic basis.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, Washington, USA, pp. 207–216. ACM Press, New York (1993)
Hamrouni, T., Yahia, S.B., Slimani, Y.: Prince: Extraction optimisée des bases génériques de régles sans calcul de fermetures. In: Proceedings of the Intl. INFORSID Conference, Hermés, Grenoble, France, pp. 353–368 (2005)
Amir, A., Feldman, R., Kashi, R.: A new versatile method for association generation. In: Princ. of Data Mining and Knowledge Disc., pp. 221–231 (1997)
Brin, S., Motawni, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlation. In: Proceedings of the SIGMOD, Tucson, Arizona (USA), pp. 265–276 (1997)
Savasere, A., Omiecinski, E., Navathe, S.B.: Mining for strong negative associa- tions in a large database of customer transactions. In: ICDE, Orlando, Florida, USA, pp. 494–502 (1998)
Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems, 381–405 (2004)
Antonie, M.L., Zaiane, O.R.: Mining positive and negative association rules: An approach for confined rules. Technical report, University of Alberta, Department of Computing Science (2004)
Kryszkiewicz, M., Cichon, K.: Support oriented discovery of generalized disjunction-free representation of frequent patterns with negation. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 672–682. Springer, Heidelberg (2005)
Kryszkiewicz, M.: Generalized disjunction-free representation of frequents patterns with at most k negations. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 468–472. Springer, Heidelberg (2006)
Morzy, M.: Efficient mining of dissociation rules. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, Springer, Heidelberg (2006)
Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)
Bouker, S., Gasmi, G., Yahia, S.B., Nguifo, E.M.: Extraction des bases génériques de régles dŠassociation généralisées. In: EGC 2007, Namur, Belgium (2007)
Bastide, Y., Pasquier, N., Taouil, R., Lakhal, L., Stumme, G.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Proceedings of the Intl. Conference DOOD 2000. LNCS, pp. 972–986. Springer, Heidelberg (2000)
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Gasmi, G., Yahia, S.B., Nguifo, E.M., Bouker, S. (2007). Extraction of Association Rules Based on Literalsets. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_27
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DOI: https://doi.org/10.1007/978-3-540-74553-2_27
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