Abstract
The negative association rules are less explored compared to the positive rules. The existing models are limited to the structure of binary data requiring of the repetitive accesses to the context, and the traditional couple support-confiance which is not effective in the presence of the dense data. For that, we propose a new model of optimization by using a new structure of data, noted MatriceSupport, and a new more selective couple, support-\(M_{GK}\).
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th VLDB Conference, Santiago, Chile, pp. 487–499 (1994)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference. SIGKDD Explor. 2(2), 66–75 (2000)
Bemarisika, P.: Extraction de régles d’association selon le couple support-\(M_{GK}\): Graphes implicatifs et Application en didactique des mathématiques. Université d’Antsiranana, Madagascar (2016)
Bemarisika, P., Totohasina, A.: Apport des régles négatives l’extraction des régles d’association. SFC 2014, CNRST-Raba-Maroc, pp. 99–104 (2014)
Bemarisika, P., Totohasina, A.: Eomf: Un algorithme d’extraction optimiseée des motifs fréquents. In: Proceedings of AAFD & SFC, Marrakech Maroc, pp. 198–203 (2016)
Boulicaut, J.-F., Bykowski, A., Jeudy, B.: Towards the tractable discovery of association rules with negations. In: Larsen, H.L., Andreasen, T., Christiansen, H., Kacprzyk, J., Zadrożny, S. (eds.) Conference on FQAS 2000. Advances in Soft Computing, vol. 7, pp. 425–434. Springer, Heidelberg (2000)
Brin, S., Motwani, R., Silverstein, C.: Bayond market baskets: Generalizing association rules to correlation. In: Proceedings of the ACM SIGMOD, pp. 265–276 (1997)
Cornelis, C., Yan, P., Zhang, X., Chen, G.: Mining Positive and Negative Association Rules from Large Databases. Proceedings of the IEEE, pp. 613–618 (2006)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, New York (1999)
Guillaume, S.: Traitement de données volumineuses. Mesure et algorithmes d’extraction de régles d’association et régles ordinales. Université de Nantes (2000)
Guillaume, S., Papon, P.-A.: Extraction optimiséé de Régles d’Association Positives et Négatives (RAPN). Clermont Université, LIMOS (2013)
Totohasina, A., Ralambondrainy, H.: ION: A pertinent new measure for mining information from many types of data. IEEE, SITIS, pp. 202–2007 (2005)
Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst. 3, 381–405 (2004)
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Bemarisika, P., Totohasina, A. (2017). Optimized Mining of Potential Positive and Negative Association Rules. In: Bellatreche, L., Chakravarthy, S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science(), vol 10440. Springer, Cham. https://doi.org/10.1007/978-3-319-64283-3_31
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DOI: https://doi.org/10.1007/978-3-319-64283-3_31
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