R. Agrawal, T. Imielinski, and A. N. Swami, Mining association rules between sets of items in large databases, Proc. of ACM SIGMOD, pp.207-216, 1993.

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proceedings of 20th VLDB Conference, pp.487-499, 1994.

M. Antonie and O. R. Zaïane, Mining Positive and Negative Association Rules: An Approach for Confined Rules, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD, pp.27-38, 2004.
DOI : 10.1007/978-3-540-30116-5_6

URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-30116-5_6.pdf

Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, PASCAL: un algorithme d'extraction des motifs fréquents, pp.65-95, 2002.

P. Bemarisika and A. Totohasina, Eomf, un algorithme d'extraction optimisée des motifs fréquents, Proc. of AAFD & SFC, Marrakech Maroc, pp.198-203, 2016.

P. Bemarisika, Extraction de règles d'association selon le couple support-MGK : Graphes implicatifs et Application en didactique des mathématiques, 2016.

P. Bemarisika and A. Totohasina, Optimisation de l'extraction des règles d'association positives et négatives, Actes des 24èmes Rencontres de la Société Francophone de Classification, pp.25-28, 2017.

P. Bemarisika and A. Totohasina, Optimized Mining of Potential Positive and Negative Association Rules, International Conference on Big Data Analytics and Knowledge Discovery, pp.424-432, 2017.
DOI : 10.1007/978-3-319-64283-3_31

URL : https://hal.archives-ouvertes.fr/hal-01858858

J. Boulicaut, A. Bykowski, and B. Jeud, Towards the tractable discovery of association rules with negations, Conference on FQAS'00, pp.425-434, 2000.

S. Brin, R. Motwani, and C. Silverstein, Bayond market baskets: Generalizing association rules to correlation, Proc. of the ACM SIGMOD, pp.265-276, 1997.

C. Cornelis, P. Yan, X. Zhang, and G. Chen, Mining Positive and Negative Association Rules from Large Databases, Proceedings of the IEEE, pp.613-618, 2006.
DOI : 10.1109/iccis.2006.252251

B. Ganter and R. Wille, Formal concept analysis: Mathematical foundations, 1999.

S. Guillaume, Traitement de données volumineuses: Mesure et algorithmes d'extraction de règles d'association, 2000.

S. Guillaume and P. Papon, Extraction optimisée de règles d'association positives et négatives (RAPN), Actes de la 13e Conf. Int. Franco. EGC, pp.157-168, 2013.

A. Savasere, E. Omiecinski, and S. Navathe, Mining for strong negative associations in a large database of customer transactions, Proc. of ICDE, pp.494-502, 1998.

W. Teng, H. Ming-jyh, and C. Ming-syan, A statistical framework for mining substitution rules, Knowl. Inf. Syst, issue.7, pp.158-178, 2005.

A. Totohasina and H. Ralambondrainy, ION, A pertinent new measure for mining information from many types of data, IEEE, SITIS, pp.202-207, 2005.

. Wilhelmiina, Kingfisher: An efficient algorithm for searching for both positive and negative dependence rules with statistical significance measures, Knowl. Inf. Syst, pp.383-414, 2012.

X. Wu, C. Zhang, S. Zhang, and S. , Efficient mining of both positive and negative association rules, In ACM Transactions on information Systems, vol.3, pp.381-405, 2004.