J. C. Bezdek, R. Ehrlich, and W. Fulls, FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, vol.10, issue.2-3, pp.191-203, 1984.
DOI : 10.1016/0098-3004(84)90020-7

T. Denoeux, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics, vol.25, issue.5, pp.804-813, 1995.
DOI : 10.1109/21.376493

T. Denoeux and M. Masson, EVCLUS: Evidential Clustering of Proximity Data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.95-109, 2004.
DOI : 10.1109/TSMCB.2002.806496

S. Ben-hariz, Z. Elouedi, and K. Mellouli, Clustering Approach Using Belief Function Theory, pp.162-171, 2006.
DOI : 10.1007/11861461_18

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, The elements of statistical learning; data mining, inference and prediction, 2001.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, p.11, 1967.

M. Masson and T. Denoeux, Clustering interval-valued proximity data using belief functions, Pattern Recognition Letters, vol.25, issue.2, pp.163-171, 2004.
DOI : 10.1016/j.patrec.2003.09.008

J. Schubert, Clustering belief functions based on attracting and conflicting metalevel evidence, Intelligent Systems for Information Processing: From Representation to Applications, 2003.
DOI : 10.1016/B978-044451379-3/50029-7

G. Shafer, Mathematical Theory of evidence, Princeton Univ, 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

L. M. Zouhal and T. Denoeux, An evidence-theoretic k-NN rule with parameter optimization, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.2, pp.263-271, 1998.
DOI : 10.1109/5326.669565