M. Bikdash, A highly interpretable form of Sugeno inference systems, IEEE Transactions on Fuzzy Systems, vol.7, issue.6, pp.686-696, 1999.
DOI : 10.1109/91.811237

J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, Interpretability Issues in Fuzzy Modeling, of Studies in Fuzziness and Soft Computing, 2003.
DOI : 10.1007/978-3-540-37057-4

S. Chen, S. A. Billings, and W. Luo, Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control, vol.10, issue.5, pp.1873-1896, 1989.
DOI : 10.2307/2284566

S. Chen, C. F. Cowan, and P. M. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, vol.2, issue.2, pp.302-309, 1991.
DOI : 10.1109/72.80341

URL : http://eprints.soton.ac.uk/251135/1/00080341.pdf

J. Valente and . Oliveira, Semantic constraints for membership functions optimization, IEEE Transactions on Systems, Man and Cybernetics. Part A, vol.29, issue.1, pp.128-138, 1999.

W. Duch, R. Setiono, and J. Zurada, Computational intelligence methods for rulebased data understanding, Proceedings of the IEEE, pp.771-805, 2004.

P. Ein-dor and J. Feldmesser, Attributes of the performance of central processing units: a relative performance prediction model, Communications of the ACM, vol.30, issue.4, pp.308-317, 1987.
DOI : 10.1145/32232.32234

J. Espinosa and J. Vandewalle, Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm, IEEE Transactions on Fuzzy Systems, vol.8, issue.5, pp.591-600, 2000.
DOI : 10.1109/91.873582

P. Glorennec, Algorithmes d'apprentissage pour systèmes d'inférence floue, Editions Hermès, 1999.

P. Glorennec, Constrained Optimization of Fuzzy Decision Trees, pp.125-139, 2003.
DOI : 10.1007/978-3-540-37057-4_6

S. Guillaume, Designing fuzzy inference systems from data: An interpretability-oriented review, IEEE Transactions on Fuzzy Systems, vol.9, issue.3, pp.426-443, 2001.
DOI : 10.1109/91.928739

URL : https://hal.archives-ouvertes.fr/hal-01320328

S. Guillaume and B. Charnomordic, Generating an Interpretable Family of Fuzzy Partitions From Data, IEEE Transactions on Fuzzy Systems, vol.12, issue.3, pp.324-335, 2004.
DOI : 10.1109/TFUZZ.2004.825979

URL : https://hal.archives-ouvertes.fr/hal-01318299

D. Han, I. D. Cluckie, D. Karbassioum, J. Lawry, and B. Krauskopf, River flow modeling using fuzzy decision trees, Water Resources Management, vol.16, issue.6, pp.431-445, 2002.
DOI : 10.1023/A:1022251422280

A. John, M. A. Hartigan, and . Wong, A k-means clustering algorithm, Applied Statistics, vol.28, pp.100-108, 1979.

J. Hohensohn and J. M. , Two-pass orthogonal least-squares algorithm to train and reduce fuzzy logic systems, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, pp.696-700, 1994.
DOI : 10.1109/FUZZY.1994.343651

F. Kossak, M. Drobics, and T. Natschlger, Extracting knowledge and computable models from data -needs, expectations, and experience, Proc. IEEE Int. Conf. on Fuzzy Systems, pp.493-498, 2004.

S. Medasani, J. Kim, and R. Krishnapuram, An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning, vol.19, issue.3-4, pp.391-417, 1998.
DOI : 10.1016/S0888-613X(98)10017-8

G. Miller, The magical number seven, plus or minus two: some limits on our capacity for processing information., Psychological Review, vol.63, issue.2, pp.81-97, 1956.
DOI : 10.1037/h0043158

W. Pedrycz, Logic-driven fuzzy modeling with fuzzy multiplexers, Engineering Applications of Artificial Intelligence, vol.17, issue.4, pp.383-391, 2004.
DOI : 10.1016/j.engappai.2004.04.011

W. Pedrycz, Fuzzy control and fuzzy systems Studies in Fuzziness, 1993.

W. Pedrycz, Why triangular membership functions? Fuzzy sets and Systems, pp.21-30, 1994.
DOI : 10.1016/0165-0114(94)90003-5

J. Quinlan, Combining Instance-Based and Model-Based Learning, Proceedings of the 10th ICML, pp.236-243, 1993.
DOI : 10.1016/B978-1-55860-307-3.50037-X

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

E. H. Ruspini, A new approach to clustering, Information and Control, vol.15, issue.1, pp.22-32, 1969.
DOI : 10.1016/S0019-9958(69)90591-9

R. Setiono and J. Y. Thong, An approach to generate rules from neural networks for regression problems, European Journal of Operational Research, vol.155, issue.1, pp.239-250, 2004.
DOI : 10.1016/S0377-2217(02)00792-0

M. Setnes, Simplification and reduction of fuzzy rules, pp.278-302
DOI : 10.1007/978-3-540-37057-4_12

L. Wang and J. M. Mendel, Fuzzy basis functions, universal approximation, and orthogonal least-squares learning, IEEE Transactions on Neural Networks, vol.3, issue.5, pp.807-814, 1992.
DOI : 10.1109/72.159070

P. J. Woolf and Y. Wang, A fuzzy logic approach to analyzing gene expression data, Physiological Genomics, vol.3, pp.9-15, 2000.

J. Yen and L. Wang, Simplifying fuzzy rule-based models using orthogonal transformation methods, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.29, issue.1, pp.13-24, 1999.
DOI : 10.1109/3477.740162