S. Arlot and P. Massart, Data-driven calibration of penalties for least-squares regression, J. Mach. Learn. Res, vol.10, pp.245-279, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00287631

A. Asuncion and D. J. Newman, UCI Machine Learning Repository, 2007.

Z. Bai, C. R. Rao, and Y. Wu, Model selection with data-oriented penalty, Journal of Statistical Planning and Inference, vol.77, issue.1, pp.102-117, 1999.
DOI : 10.1016/S0378-3758(98)00168-2

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

L. Birgé and P. Massart, Minimal Penalties for Gaussian Model Selection, Probability Theory and Related Fields, vol.6, issue.1-2, pp.33-73, 2007.
DOI : 10.1007/s00440-006-0011-8

G. Celeux and G. Govaert, Clustering criteria for discrete data and latent class models, Journal of Classification, vol.4, issue.4, pp.157-176, 1991.
DOI : 10.1007/BF02616237
URL : https://hal.archives-ouvertes.fr/inria-00075437

G. Celeux, M. Hurn, and C. P. Robert, Computational and Inferential Difficulties with Mixture Posterior Distributions, Journal of the American Statistical Association, vol.60, issue.451, pp.957-970, 2000.
DOI : 10.1080/01621459.1995.10476589
URL : https://hal.archives-ouvertes.fr/inria-00073049

C. Chen, F. Forbes, and O. Francois, fastruct: model-based clustering made faster, Molecular Ecology Notes, vol.58, issue.4, pp.980-983, 2006.
DOI : 10.1111/j.1471-8286.2006.01527.x

L. M. Collins and S. T. Lanza, Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences, 2010.
DOI : 10.1002/9780470567333

J. Corander, P. Marttinen, J. Sirén, and J. Tang, Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations, BMC Bioinformatics, vol.9, issue.1, p.539, 2008.
DOI : 10.1186/1471-2105-9-539

A. P. Dempster, N. M. Lairdsand, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Royal Statist. Soc. Series B, vol.39, pp.1-38, 1977.

C. R. Genoveve and L. Wasserman, Rates of convergence for the Gaussian mixture sieve, Ann. Statist, vol.28, pp.1105-1127, 2000.

L. A. Goodman, E. K. Latch, G. Dharmarajan, J. C. Glaubitz, O. E. Rhodes et al., Exploratory latent structure analysis using both identifiable and unidentifiable models Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation, Biometrika Conservation Genetics, vol.61, issue.7, pp.215-231, 1974.

´. E. Lebarbier, Quelques approches pour la détection de rupturè a horizon fini PhD thesis, 2002.

P. Massart, Concentration inequalities and model selection, Lecture Notes in Mathematics, vol.1896, 2007.

C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection, ESAIM: Probability and Statistics, vol.15, pp.41-68, 2011.
DOI : 10.1051/ps/2009004
URL : https://hal.archives-ouvertes.fr/inria-00284613

C. Maugis and B. Michel, Data-driven penalty calibration: A case study for Gaussian mixture model selection, ESAIM: Probability and Statistics, vol.15, pp.320-339, 2011.
DOI : 10.1051/ps/2010002
URL : https://hal.archives-ouvertes.fr/hal-00666813

A. L. Mccutcheon, Latent Class Analysis, Quantitative Applications in the Social Sciences, vol.64, 1987.

G. Mclachlan and D. Peel, Finite Mixture Models, p.1789474, 2000.
DOI : 10.1002/0471721182

M. Nadif and G. Govaert, Clustering for binary data and mixture models???choice of the model, Applied Stochastic Models and Data Analysis, vol.13, issue.3-4, pp.269-278, 1998.
DOI : 10.1002/(SICI)1099-0747(199709/12)13:3/4<269::AID-ASM321>3.0.CO;2-7

J. K. Pritchard, M. Stephens, and P. Donnelly, Inference of population structure using multilocus genotype data, Genetics, vol.155, pp.945-59, 2000.

L. Rigouste, O. Cappé, and F. Yvon, Inference and evaluation of the multinomial mixture model for text clustering, Information Processing & Management, vol.43, issue.5, pp.1260-1280, 2006.
DOI : 10.1016/j.ipm.2006.11.001
URL : https://hal.archives-ouvertes.fr/hal-00080133

N. A. Rosenberg, T. Burke, K. Elo, M. W. Feldman, P. J. Freidlin et al., Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds, Biotechnology, 2001.

W. Toussile and E. Gassiat, Variable selection in model-based clustering using multilocus genotype data, Advances in Data Analysis and Classification, vol.77, issue.2, pp.109-134, 2009.
DOI : 10.1007/s11634-009-0043-x

N. Verzelen, Adaptative estimation to regular Gaussian Markov random fields PhD thesis, 2009.

F. Villers, Tests et selection de modèles pour l'analyse de données protéomiques et transcriptomiques, 2007.