H. Akaike, Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory (Tsahkadsor, pp.267-281, 1971.

S. Arlot and F. Bach, Data-driven calibration of linear estimators with minimal penalties, Advances in Neural Information Processing Systems (NIPS), pp.22-46, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00414774

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

B. Auder and A. Fischer, Projection-based curve clustering, Journal of Statistical Computation and Simulation, vol.82, issue.8, 2011.
DOI : 10.1093/biomet/87.1.135
URL : https://hal.archives-ouvertes.fr/hal-00565541

F. Bach, Bolasso, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.33-40, 2008.
DOI : 10.1145/1390156.1390161
URL : https://hal.archives-ouvertes.fr/hal-00271289

A. Barron, L. Birgé, and P. Massart, Risk bounds for model selection via penalization. Probability Theory and Related Fields, pp.301-413, 1999.

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

K. Bertin, L. Pennec, E. , R. , and V. , Adaptive Dantzig density estimation, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, pp.43-74, 2011.
DOI : 10.1214/09-AIHP351
URL : https://hal.archives-ouvertes.fr/hal-00381984

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software, Computational Statistics & Data Analysis, vol.51, issue.2, pp.587-600, 2006.
DOI : 10.1016/j.csda.2005.12.015
URL : https://hal.archives-ouvertes.fr/inria-00069878

L. Birgé and P. Massart, Minimal penalties for Gaussian model selection. Probability Theory and Related Fields, pp.33-73, 2006.

L. Birgé and P. Massart, From Model Selection to Adaptive Estimation, pp.55-87, 1997.
DOI : 10.1007/978-1-4612-1880-7_4

M. J. Brusco and J. D. Cradit, A variable-selection heuristic for K-means clustering, Psychometrika, vol.63, issue.2, pp.249-270, 2001.
DOI : 10.1007/BF02294838

C. Caillerie and B. Michel, Model Selection for Simplicial Approximation, Foundations of Computational Mathematics, vol.33, issue.2, pp.707-731, 2011.
DOI : 10.1007/s10208-011-9103-7
URL : https://hal.archives-ouvertes.fr/inria-00402091

G. Castellan, Modified Akaike's criterion for histogram density estimation, 1999.

P. Connault, Calibration d'algorithmes de type Lasso et analyse statistique de données métallurgiques en aéronautique, 2011.

M. Dash, K. Choi, P. Scheuermann, and H. Liu, Feature selection for clustering - a filter solution, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.115-122, 2002.
DOI : 10.1109/ICDM.2002.1183893

A. P. Dempster, N. M. Laird, R. , and D. B. , Maximum likelihood from incomplete data via the EM algorithm (with discussion), Journal of the Royal Statistical Society. Series B, vol.39, issue.1, pp.1-38, 1977.

M. Devaney and A. Ram, Efficient feature selection in conceptual clustering, Machine Learning: Proceedings of the Fourteenth International Conference, pp.92-97, 1997.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression. The Annals of statistics, pp.407-499, 2004.

E. B. Fowlkes, R. Gnanadesikan, and J. R. Kettenring, Variable selection in clustering, Journal of Classification, vol.26, issue.2, pp.205-228, 1988.
DOI : 10.1007/BF01897164

C. Fraley and A. Raftery, Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST, Journal of Classification, vol.20, issue.2, pp.263-286, 2003.
DOI : 10.1007/s00357-003-0015-3

P. Jouve and N. Nicoloyannis, A Filter Feature Selection Method for Clustering, Proceedings of International Symposium on Methodologies for Intelligent Systems, pp.583-593, 2005.
DOI : 10.1007/11425274_60

S. Kim, M. G. Tadesse, and M. Vannucci, Variable selection in clustering via Dirichlet process mixture models, Biometrika, vol.93, issue.4, pp.877-893, 2006.
DOI : 10.1093/biomet/93.4.877

E. Lebarbier, Detecting multiple change-points in the mean of Gaussian process by model selection, Signal Processing, vol.85, issue.4, pp.717-736, 2005.
DOI : 10.1016/j.sigpro.2004.11.012
URL : https://hal.archives-ouvertes.fr/inria-00071847

M. Lerasle, Optimal model selection for density estimation of stationary data under various mixing conditions, The Annals of Statistics, vol.39, issue.4, pp.1852-1877, 2011.
DOI : 10.1214/11-AOS888SUPP
URL : https://hal.archives-ouvertes.fr/hal-00656405

M. Lerasle, Optimal model selection in density estimation, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.48, issue.3, pp.884-908, 2012.
DOI : 10.1214/11-AIHP425
URL : https://hal.archives-ouvertes.fr/hal-00422655

P. Massart, Concentration inequalities and model selection, Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, 2003.

C. Maugis, Sélection de variables pour la classification non supervisée par mélanges gaussiens. Application à l'étude de données transcriptomes, 2008.

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

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, G. Celeux, and M. Martin-magniette, Variable Selection for Clustering with Gaussian Mixture Models, Biometrics, vol.100, issue.3, pp.701-709, 2009.
DOI : 10.1111/j.1541-0420.2008.01160.x
URL : https://hal.archives-ouvertes.fr/inria-00153057

C. Maugis, G. Celeux, and M. Martin-magniette, Variable selection in model-based clustering: A general variable role modeling, Computational Statistics & Data Analysis, vol.53, issue.11, pp.3872-3882, 2009.
DOI : 10.1016/j.csda.2009.04.013
URL : https://hal.archives-ouvertes.fr/inria-00342108

M. Misiti, Y. Misiti, G. Oppenheim, and J. Poggi, Clustering Signals Using Wavelets, Proceedings of the 9th international work conference on Artificial neural networks, pp.514-521, 2007.
DOI : 10.1007/978-3-540-73007-1_63

M. Misiti, Y. Misiti, G. Oppenheim, and J. Poggi, Wavelets and their Applications, 2007.
DOI : 10.1002/9780470612491

W. Pan and X. Shen, Penalized model-based clustering with application to variable selection, Journal of Machine Learning Research, vol.8, pp.1145-1164, 2007.

A. E. Raftery and N. Dean, Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, vol.101, issue.473, pp.168-178, 2006.
DOI : 10.1198/016214506000000113

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

M. G. Tadesse, N. Sha, and M. Vannucci, Bayesian Variable Selection in Clustering High-Dimensional Data, Journal of the American Statistical Association, vol.100, issue.470, pp.602-617, 2005.
DOI : 10.1198/016214504000001565

N. Verzelen, Data-driven neighborhood selection of a Gaussian field, Computational Statistics & Data Analysis, vol.54, issue.5, pp.1355-1371, 2010.
DOI : 10.1016/j.csda.2009.12.001
URL : https://hal.archives-ouvertes.fr/inria-00353260

M. Yuan and Y. Lin, On the non-negative garrotte estimator, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.101, issue.2, pp.143-161, 2007.
DOI : 10.1111/j.1467-9868.2005.00503.x

P. Zhao and B. Yu, On model selection consistency of lasso, Journal of Machine Learning Research, vol.7, pp.2541-2567, 2007.

H. Zhou, W. Pan, and X. Shen, Penalized model-based clustering with unconstrained covariance matrices, Electronic Journal of Statistics, vol.3, issue.0, pp.1473-1496, 2009.
DOI : 10.1214/09-EJS487
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2867492

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.404