H. Akaike, Information theory and an extension of the maximum likelihood principle, Proceedings, 2nd Internat. Symp. on Information Theory, pp.267-281, 1973.

S. Arlot, Resampling and model selection, 2007.
URL : https://hal.archives-ouvertes.fr/tel-00198803

J. Baudry, A. Raftery, G. Celeux, K. Lo, and R. Gottardo, Combining Mixture Components for Clustering, Journal of Computational and Graphical Statistics, vol.19, issue.2, pp.332-353, 2010.
DOI : 10.1198/jcgs.2010.08111

URL : https://hal.archives-ouvertes.fr/inria-00321090

J. Baudry, Model Selection for Clustering Choosing the Number of Classes, 2009.
URL : https://hal.archives-ouvertes.fr/tel-00461550

J. Baudry, G. Celeux, M. , and J. , Selecting models focussing on the modeler's purpose, COMPSTAT 2008: Proceedings in Computational Statistics, pp.337-348, 2008.
DOI : 10.1007/978-3-7908-2084-3_28

J. Baudry, C. Maugis, M. , and B. , Slope heuristics: overview and implementation, Statistics and Computing, vol.6, issue.2, pp.455-470, 2011.
DOI : 10.1007/s11222-011-9236-1

URL : https://hal.archives-ouvertes.fr/hal-00461639

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

C. Biernacki and G. Govaert, Using the classification likelihood to choose the number of clusters, Computing Science and Statistics, vol.29, pp.451-457, 1997.

L. Birgé and P. Massart, Minimal penalties for Gaussian model selection. Probab. Theory Related Fields, pp.33-73, 2007.

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995.
DOI : 10.1016/0031-3203(94)00125-6

URL : https://hal.archives-ouvertes.fr/inria-00074643

C. De-granville, J. Southerland, and A. Fagg, Learning grasp affordances through human demonstration, Proceedings of the International Conference on Development and Learning, 2006.

C. Fraley and A. Raftery, Model-Based Clustering, Discriminant Analysis, and Density Estimation, Journal of the American Statistical Association, vol.97, issue.458, pp.611-631, 2002.
DOI : 10.1198/016214502760047131

C. Goutte, L. Hansen, M. Liptrot, R. , and E. , Feature-space clustering for fMRI meta-analysis, Human Brain Mapping, vol.41, issue.3, pp.165-183, 2001.
DOI : 10.1002/hbm.1031

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

T. Hamelryck, J. T. Kent, and A. Krogh, Sampling Realistic Protein Conformations Using Local Structural Bias, PLoS Computational Biology, vol.265, issue.9, p.131, 2006.
DOI : 10.1371/journal.pcbi.0020131.sd002

URL : http://doi.org/10.1371/journal.pcbi.0020131.eor

C. Hennig, Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, vol.12, issue.1, pp.3-34, 2010.
DOI : 10.1007/s11634-010-0058-3

M. Mariadassou, S. Robin, and C. Vacher, Uncovering latent structure in valued graphs: A variational approach, The Annals of Applied Statistics, vol.4, issue.2, pp.715-742, 2010.
DOI : 10.1214/07-AOAS361SUPP

URL : https://hal.archives-ouvertes.fr/hal-01197514

P. Massart, Concentration Inequalities and Model Selection, Lecture Notes in Math, 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

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

A. Mcquarrie and C. Tsai, Regression and time series model selection, 1998.
DOI : 10.1142/3573

R. Nishii, Maximum likelihood principle and model selection when the true model is unspecified, Journal of Multivariate Analysis, vol.27, issue.2, pp.392-403, 1988.
DOI : 10.1016/0047-259X(88)90137-6

A. Pigeau and M. Gelgon, Building and tracking hierarchical geographical & temporal partitions for image collection management on mobile devices, Proceedings of the 13th annual ACM international conference on Multimedia , MULTIMEDIA '05, pp.141-150, 2005.
DOI : 10.1145/1101149.1101170

URL : https://hal.archives-ouvertes.fr/hal-00486129

R. Redner and H. Walker, Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984.
DOI : 10.1137/1026034

G. Rigaill, E. Lebarbier, R. , and S. , Exact posterior distributions and model selection criteria for multiple change-point detection problems, Statistics and Computing, vol.63, issue.1, pp.1-13, 2012.
DOI : 10.1007/s11222-011-9258-8

URL : https://hal.archives-ouvertes.fr/hal-01000030

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

R. J. Steele and A. Raftery, Performance of bayesian model selection criteria for gaussian mixture models, Frontiers of Statistical Decision Making and Bayesian Analysis, pp.113-130, 2010.

D. Titterington, A. Smith, and U. Makov, Statistical Analysis of Finite mixture Distributions, 1985.

A. Van-der-vaart, Asymptotic Statistics, 1998.
DOI : 10.1017/CBO9780511802256