. Abraham, Unsupervised Curve Clustering using B-Splines, Scandinavian Journal of Statistics, vol.78, issue.3, pp.581-595, 2003.
DOI : 10.1111/1467-9469.00350

S. Abramowitz, M. Abramowitz, and I. Stegun, Handbook of Mathematical Functions, American Journal of Physics, vol.34, issue.2, 1970.
DOI : 10.1119/1.1972842

J. Blei, D. M. Blei, and M. I. Jordan, Variational inference for Dirichlet process mixtures, Bayesian Analysis, vol.1, issue.1, pp.121-144, 2005.
DOI : 10.1214/06-BA104

. Cadez, A general probabilistic framework for clustering individuals and objects, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.140-149, 2000.
DOI : 10.1145/347090.347119

. Chamroukhi, A hidden process regression model for functional data description. Application to curve discrimination, Neurocomputing, vol.73, issue.7-9, pp.7-91210, 2010.
DOI : 10.1016/j.neucom.2009.12.023

URL : https://hal.archives-ouvertes.fr/hal-00485163

. Chapman, CRISP-DM 1.0 : step-by-step data mining guide, 2000.

. Cover, . Thomas, T. Cover, and J. Thomas, Elements of information theory, 1991.

G. Delaigle and P. Hall, Defining probability density for a distribution of random functions, The Annals of Statistics, vol.38, issue.2, pp.1171-1193, 2010.
DOI : 10.1214/09-AOS741

V. Ferraty, F. Ferraty, and P. Vieu, Nonparametric Functional Data Analysis: Theory and Practice, 2006.

S. Gaffney, S. Gaffney, and P. Smyth, Joint probabilistic curve clustering and alignment, Advances in Neural Information Processing Systems 17, 2004.

. Gasser, Nonparametric estimation of the mode of a distribution of random curves, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.60, issue.4, pp.681-691, 1998.
DOI : 10.1111/1467-9868.00148

M. Hansen, P. Hansen, and N. Mladenovic, Variable neighborhood search: Principles and applications, European Journal of Operational Research, vol.130, issue.3, pp.449-467, 2001.
DOI : 10.1016/S0377-2217(00)00100-4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.93.1769

. Hastie, The elements of statistical learning, 2001.

. Hébrail, Exploratory analysis of functional data via clustering and optimal segmentation, Neurocomputing, vol.73, issue.7-9, pp.7-91125, 2010.
DOI : 10.1016/j.neucom.2009.11.022

R. M. Neal, Markov chain sampling methods for dirichlet process mixture models, Journal of Computational AND Graphical Statistics, vol.9, issue.2, pp.249-265, 2000.

G. Nguyen, X. Nguyen, and A. Gelfand, The Dirichlet labeling process for clustering functional data, Statistica Sinica, vol.21, issue.3, pp.1249-1289, 2011.
DOI : 10.5705/ss.2008.285

R. , S. Ramsay, J. Silverman, and B. , Functional Data Analysis, 2005.

J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

. Sheather, . Jones, S. Sheather, and M. Jones, A reliable data-based bandwidth selection method for kernel density estimation, Journal of the Royal Statistical Society. Series B, pp.683-690, 1991.

. Vogt, Dirichlet processes The translationinvariant wishart-dirichlet process for clustering distance data, Encyclopedia of Machine Learning, 2010.

. Wallach, An alternative prior process for nonparametric bayesian clustering, AISTATS, pp.892-899, 2010.