A. Samet and T. T. Dao, Mining over a Reliable Evidential Database: Application on Amphiphilic Chemical Database, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp.1257-1262, 2015.
DOI : 10.1109/ICMLA.2015.31

C. C. Aggarwal and J. Han, Frequent pattern mining, 2014.
DOI : 10.1007/978-3-319-07821-2

R. Agrawal and R. Srikant, Fast algorithm for mining association rules, Proceedings of international conference on Very Large DataBases, VLDB, pp.487-499, 1994.

C. C. Aggarwal, Y. Li, J. Wang, and J. Wang, Frequent pattern mining with uncertain data, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.29-38, 2009.
DOI : 10.1145/1557019.1557030
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.7190

D. A. Bell, J. Guan, and S. K. Lee, Generalized union and project operations for pooling uncertain and imprecise information, Data & Knowledge Engineering, vol.18, issue.2, pp.89-117, 1996.
DOI : 10.1016/0169-023X(95)00029-R

C. K. Chui, B. Kao, and E. Hung, Mining Frequent Itemsets from Uncertain Data, Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp.47-58, 2007.
DOI : 10.1007/978-3-540-71701-0_8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.6035

C. C. Aggarwal, Managing and Mining Uncertain Data, 2010.
DOI : 10.1007/978-0-387-09690-2

K. R. Hewawasam, K. Premaratne, and M. L. Shyu, Rule Mining and Classification in a Situation Assessment Application: A Belief-Theoretic Approach for Handling Data Imperfections, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.37, issue.6, pp.1446-1459, 2007.
DOI : 10.1109/TSMCB.2007.903536

Y. Chen and C. Weng, Mining association rules from imprecise ordinal data, Fuzzy Sets and Systems, vol.159, issue.4, pp.460-474, 2008.
DOI : 10.1016/j.fss.2007.10.005

M. A. Bach-tobji, B. Ben-yaghlane, and K. Mellouli, Incremental Maintenance of Frequent Itemsets in Evidential Databases, Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.457-468, 2009.
DOI : 10.1002/(SICI)1098-111X(199802/03)13:2/3<127::AID-INT3>3.0.CO;2-1

A. Dempster, Upper and lower probabilities induced by multivalued mapping, AMS, p.38, 1967.
DOI : 10.1007/978-3-540-44792-4_3

G. Shafer, A Mathematical Theory of Evidence, 1976.

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994.
DOI : 10.1016/0004-3702(94)90026-4
URL : https://hal.archives-ouvertes.fr/hal-01185821

D. Dubois and H. Prade, The principle of minimum specificity as a basis for evidential reasoning, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.75-84, 1986.
DOI : 10.1007/3-540-18579-8_6

T. N. Hoang, T. T. Dao, and M. C. Ho-ba-tho, Clustering of Children with Cerebral Palsy with Prior Biomechanical Knowledge Fused from Multiple Data Sources, Proceedings of 5th International Symposium Integrated Uncertainty in Knowledge Modelling and Decision Making, pp.359-370, 2016.
DOI : 10.1109/TSMCB.2002.806496

A. Samet, E. Lefèvre, and S. Ben-yahia, Evidential data mining: precise support and confidence, Journal of Intelligent Information Systems, vol.12, issue.11, pp.1-29, 2016.
DOI : 10.1016/j.is.2003.10.001

P. Smets, The application of the matrix calculus to belief functions, International Journal of Approximate Reasoning, vol.31, issue.1-2, pp.1-30, 2002.
DOI : 10.1016/S0888-613X(02)00066-X