R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high-dimensional data for data mining application, ACM SIGMOD International Conference on Management of Data, pp.94-105, 1998.

J. Banfield and A. Raftery, Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, vol.49, issue.3, pp.803-821, 1993.
DOI : 10.2307/2532201

R. Bellman, Dynamic Programming, 1957.

J. C. Bezdek, C. Coray, R. Gunderson, and J. Watson, -Lines, SIAM Journal on Applied Mathematics, vol.40, issue.2, pp.339-357, 1981.
DOI : 10.1137/0140029

URL : https://hal.archives-ouvertes.fr/hal-00155958

L. Bocci, D. Vicari, and M. Vichi, A mixture model for the classification of three-way proximity data, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1625-1654, 2006.
DOI : 10.1016/j.csda.2005.02.007

H. Bock, The equivalence of two extremal problems and its application to the iterative classification of multivariate data, Mathematisches Forschungsinstitut, 1969.

H. Bock, Automatische Klassifikation, Vandenhoeck and Ruprecht, 1974.
DOI : 10.1007/978-3-642-88253-1_10

H. Bock, On the interface between cluster analysis, principal component clustering, and multidimensional scaling, Multivariate statistical modeling and data analysis, pp.7-34, 1987.

H. Bock, Probabilistic models in cluster analysis, Computational Statistics & Data Analysis, vol.23, issue.1, pp.5-28, 1996.
DOI : 10.1016/0167-9473(96)88919-5

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

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

D. Soete and J. D. Carroll, K-means clustering in a low-dimensional Euclidean space, New approaches in classification and data analysis, pp.212-219, 1994.
DOI : 10.1007/978-3-642-51175-2_24

P. Demartines and J. Hérault, Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets, IEEE Transactions on Neural Networks, vol.8, issue.1, pp.148-154, 1997.
DOI : 10.1109/72.554199

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

W. S. Desarbo and W. L. Cron, A maximum likelihood methodology for clusterwise linear regression, Journal of Classification, vol.39, issue.19, pp.249-282, 1988.
DOI : 10.1007/BF01897167

E. Diday, IntroductionàIntroductionà l'analyse factorielle typologique, pp.29-38, 1974.

B. Flury, Common Principal Components in K Groups, Journal of the American Statistical Association, vol.79, issue.388, pp.892-897, 1984.
DOI : 10.2307/2288721

B. Flury and W. Gautschi, An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form, SIAM Journal on Scientific and Statistical Computing, vol.7, issue.1, pp.169-184, 1986.
DOI : 10.1137/0907013

L. Flury, B. Boukai, and B. Flury, The Discrimination Subspace Model, Journal of the American Statistical Association, vol.17, issue.438, pp.758-766, 1997.
DOI : 10.1080/01621459.1997.10474028

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

S. Girard, A nonlinear PCA based on manifold approximation, Computational Statistics, vol.15, issue.2, pp.145-167, 2000.
DOI : 10.1007/s001800000025

URL : https://hal.archives-ouvertes.fr/hal-00724764

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

T. Hastie and W. Stuetzle, Principal Curves, Journal of the American Statistical Association, vol.26, issue.406, pp.502-516, 1989.
DOI : 10.1080/03610927508827223

A. Jain, M. Marty, and P. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

I. Jolliffe, Principal Component Analysis, 1986.
DOI : 10.1007/978-1-4757-1904-8

T. Kohonen, Self-Organizing Maps, 1995.

W. Krzanowski, P. Jonathan, W. Mccarthy, and M. Thomas, Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data, Applied Statistics, vol.44, issue.1, pp.101-115, 1995.
DOI : 10.2307/2986198

R. Lehoucq, D. Sorensen, and C. Yang, ARPACK users' guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods, 1998.
DOI : 10.1137/1.9780898719628

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, 1997.

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

G. Mclachlan, D. Peel, and R. Bean, Modelling high-dimensional data by mixtures of factor analyzers, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.379-388, 2003.
DOI : 10.1016/S0167-9473(02)00183-4

L. Parsons, E. Haque, and H. Liu, Subspace clustering for high dimensional data, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.90-105, 2004.
DOI : 10.1145/1007730.1007731

T. Pavlenko, On feature selection, curse-of-dimensionality and error probability in discriminant analysis, Journal of Statistical Planning and Inference, vol.115, issue.2, pp.565-584, 2003.
DOI : 10.1016/S0378-3758(02)00166-0

T. Pavlenko and D. Von-rosen, Effect of dimensionality on discrimination, Statistics, vol.9, issue.3, pp.191-213, 2001.
DOI : 10.1016/0031-3203(90)90100-Y

R. E. Quandt and J. B. Ramsey, Estimating Mixtures of Normal Distributions and Switching Regressions, Journal of the American Statistical Association, vol.16, issue.364, pp.730-752, 1978.
DOI : 10.1080/01621459.1978.10480085

A. 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

S. Roweis and L. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000.
DOI : 10.1126/science.290.5500.2323

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

J. Schott, Dimensionality reduction in quadratic discriminant analysis, Computational Statistics & Data Analysis, vol.16, issue.2, pp.161-174, 1993.
DOI : 10.1016/0167-9473(93)90111-6

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

D. Scott and J. Thompson, Probability density estimation in higher dimensions, Fifteenth Symposium in the Interface, pp.173-179, 1983.

J. Tenenbaum, V. D. Silva, and J. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

M. Tipping and C. Bishop, Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999.
DOI : 10.1007/BF00162527