R. Agrawal, T. Imieli?ski, and A. Swami, Mining association rules between sets of items in large databases, Acm sigmod record, vol.22, pp.207-216, 1993.

R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proc. 20th int. conf. very large data bases, VLDB, vol.1215, pp.487-499, 1994.

A. Ait-mlouk, F. Gharnati, and T. Agouti, Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach, Vietnam Journal of Computer Science, vol.3, issue.4, pp.235-245, 2016.

S. Bouker, R. Saidi, S. B. Yahia, and E. M. Nguifo, Ranking and selecting association rules based on dominance relationship, 2012 IEEE 24th international conference on tools with artificial intelligence, vol.1, pp.658-665, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00677853

M. C. Chen, Ranking discovered rules from data mining with multiple criteria by data envelopment analysis, Expert Systems with Applications, vol.33, issue.4, pp.1110-1116, 2007.

D. H. Choi, B. S. Ahn, and S. H. Kim, Prioritization of association rules in data mining: Multiple criteria decision approach, Expert Systems with Applications, vol.29, issue.4, pp.867-878, 2005.

A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, The Annals of Mathematical Statistics, vol.38, pp.325-339, 1967.

Y. Djouadi, S. Redaoui, and K. Amroun, Mining association rules under imprecision and vagueness: towards a possibilistic approach, 2007 IEEE International Fuzzy Systems Conference, pp.1-6, 2007.

D. Dubois and T. Denoeux, Conditioning in dempster-shafer theory: prediction vs. revision, Belief Functions: Theory and Applications, pp.385-392, 2012.

R. Fagin and J. Y. Halpern, A new approach to updating beliefs, Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, pp.347-374, 1991.

J. Figueira and B. Roy, Determining the weights of criteria in the electre type methods with a revised simos' procedure, European Journal of Operational Research, vol.139, issue.2, pp.317-326, 2002.

L. Geng and H. J. Hamilton, Interestingness measures for data mining: a survey, ACM Computing Surveys, vol.38, issue.3, p.9, 2006.

K. Hewawasam, K. Premaratne, S. Subasingha, and M. L. Shyu, Rule mining and classification in imperfect databases, 2005 7th International Conference on Information Fusion, vol.1, p.8, 2005.

T. P. Hong, K. Y. Lin, and S. L. Wang, Fuzzy data mining for interesting generalized association rules. Fuzzy sets and systems, vol.138, pp.255-269, 2003.

S. Kotsiantis and D. Kanellopoulos, Association rules mining: A recent overview, GESTS International Transactions on Computer Science and Engineering, vol.32, issue.1, pp.71-82, 2006.

B. Liu, W. Hsu, S. Chen, and Y. Ma, Analyzing the subjective interestigness of association rules, IEEE Intelligent Systems, vol.15, issue.5, pp.47-55, 2000.

T. T. Nguyen-le, H. X. Huynh, and F. Guillet, Finding the most interesting association rules by aggregating objective interestingness measures, Knowledge Acquisition: Approaches, Algorithms and Applications, pp.40-49, 2009.

B. Roy, Revue française d'informatique et de recherche opérationnelle, vol.2, pp.57-75, 1968.

A. Samet, E. Lefèvre, and S. B. Yahia, Evidential data mining: precise support and confidence, Journal of Intelligent Information Systems, vol.47, issue.1, pp.135-163, 2016.

N. Seco, T. Veale, and J. Hayes, An intrinsic information content metric for semantic similarity in wordnet, In: Ecai, vol.16, p.1089, 2004.

G. Shafer, A mathematical theory of evidence, vol.42, 1976.

A. Silberschatz and A. Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and data engineering, vol.8, issue.6, pp.970-974, 1996.

P. N. Tan, V. Kumar, and J. Srivastava, Selecting the right interestingness measure for association patterns, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.32-41, 2002.

M. B. Tobji, B. B. Yaghlane, and K. Mellouli, A new algorithm for mining frequent itemsets from evidential databases, Proceedings of IPMU, vol.8, pp.1535-1542, 2008.

M. A. Tobji, B. B. Yaghlane, and K. Mellouli, Frequent itemset mining from databases including one evidential attribute, International Conference on Scalable Uncertainty Management, pp.19-32, 2008.

M. Toloo, B. Sohrabi, and S. Nalchigar, A new method for ranking discovered rules from data mining by dea, Expert Systems with Applications, vol.36, issue.4, pp.8503-8508, 2009.

B. Vaillant, P. Lenca, and S. Lallich, A clustering of interestingness measures, International Conference on Discovery Science, pp.290-297, 2004.
URL : https://hal.archives-ouvertes.fr/hal-02127560