H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

E. Anderson, The irises of the gaspé peninsula, Bulletin of the American Iris Society, vol.59, pp.2-5, 1935.

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.

H. Bensmail and G. Celeux, Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition, Journal of the American Statistical Association, vol.91, issue.436, pp.1743-1748, 1996.
DOI : 10.1002/0471725293

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. Bishop, Novelty detection and neural network validation, IEES Conference on Vision and Image Signal Processing, pp.217-222, 1994.

A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, 1998.
DOI : 10.1145/279943.279962

URL : http://axon.cs.byu.edu/~martinez/classes/678/Papers/Mitchell_cotraining.pdf

C. Bouveyron, H. Chipman, and E. Côme, Supervised classification and visualization of social networks based on a probabilistic latent space model, 7th International Workshop on Mining and Learning with Graphs, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00407831

C. Bouveyron, S. Girard, and C. Schmid, High-dimensional data clustering, Computational Statistics & Data Analysis, vol.52, issue.1, pp.502-519, 2007.
DOI : 10.1016/j.csda.2007.02.009

URL : https://hal.archives-ouvertes.fr/inria-00548573

C. Bouveyron, S. Girard, and C. Schmid, High-Dimensional Discriminant Analysis, Communications in Statistics - Theory and Methods, vol.1, issue.14, pp.2607-2623, 2007.
DOI : 10.1214/aos/1176344136

URL : https://hal.archives-ouvertes.fr/inria-00548516

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. Chow, On optimum recognition error and reject tradeoff, IEEE Transactions on Information Theory, pp.41-46, 1970.
DOI : 10.1109/TIT.1970.1054406

D. Dasgupta and F. Nino, A comparison of negative and positive selection algorithms in novel pattern detection, SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166), pp.125-130, 2000.
DOI : 10.1109/ICSMC.2000.884976

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.

M. Desforges, P. Jacob, and J. Cooper, Applications of probability density estimation to the detection of abnormal conditions in engineering, Proc. Institute of Mechanical Engineers, pp.687-703
DOI : 10.1243/0954406981521448

R. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

M. Handcock, A. Raftery, and J. Tantrum, Model-based clustering for social networks, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.6, issue.2, pp.1-22, 2007.
DOI : 10.1111/j.1467-9574.2005.00283.x

L. Hansen, C. Liisberg, and P. Salamon, The error-reject tradeoff. Open Systems and Information Dynamics, pp.159-184, 1997.

K. Harris, F. Florey, J. Tabor, P. Bearman, J. Jones et al., The national longitudinal of adolescent health: Research design, 2003.

M. Hellman, The nearest neighbour classification with a reject option, IEEE Transactions on Systems Science and Cybernetics, pp.179-185, 1970.

C. Hennig and P. Coretto, The Noise Component in Model-based Cluster Analysis, Data Analysis Machine Learning and Applications, pp.127-138, 2008.
DOI : 10.1007/978-3-540-78246-9_16

P. Hoff, A. Raftery, and M. Handcock, Latent Space Approaches to Social Network Analysis, Journal of the American Statistical Association, vol.97, issue.460, pp.1090-1098, 2002.
DOI : 10.1198/016214502388618906

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.6390

T. Kohonen, Self-organisation and associative memory, 1988.

B. Krishnapuram, D. Williams, Y. Xue, A. Hartemink, L. Carin et al., On semi-supervised classification, NIPS, 2004.

C. Manikopoulos and S. Papavassiliou, Network intrusion and fault detection: a statistical anomaly approach. rk intrusion and fault detection: a IEEE Communications Magazine, pp.76-82, 2002.

M. Markou and S. Singh, Novelty detection: a review???part 1: statistical approaches, Signal Processing, vol.83, issue.12, pp.2481-2497, 2003.
DOI : 10.1016/j.sigpro.2003.07.018

M. Markou and S. Singh, Novelty detection: a review???part 2:, Signal Processing, vol.83, issue.12, pp.2499-2521, 2003.
DOI : 10.1016/j.sigpro.2003.07.019

G. Mclachlan, Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis, Journal of the American Statistical Association, vol.31, issue.350, pp.365-369, 1975.
DOI : 10.1080/01621459.1975.10479874

G. Mclachlan, Discriminant Analysis and Statistical Pattern Recognition, 1992.
DOI : 10.1002/0471725293

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

T. Odin and D. Addison, Novelty detection using neural network technology, Proc. of COMADEN conference, 2000.

T. O. Neill, Normal discrimination with unclassified observations, Journal of the American Statistical Association, issue.73, pp.821-826, 1978.

S. Roberts, Novelty detection using extreme value statistics, IEE Proc. on Vision, Image and Signal Processing, pp.124-129, 1999.
DOI : 10.1049/ip-vis:19990428

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.1338

S. Roberts and L. Tarassenko, A Probabilistic Resource Allocating Network for Novelty Detection, Neural Computation, vol.139, issue.6, pp.270-284, 1994.
DOI : 10.1007/BF02457830

J. Ryan, M. Lin, and R. Miikkulainen, Intrusion detection with neural networks, Advances in Neural Information Processing Systems, 1998.

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. Seeger, Learning with labeled and unlabeled data, 2001.

B. Shölkopf, R. Williamson, A. Smola, J. Taylor, and J. Platt, Support vector method for novelty detection, Advances in Neural Information Processing Systems, pp.582-588, 2000.

L. Tarassenko, Novelty detection for the identification of masses in mammograms, 4th International Conference on Artificial Neural Networks, pp.442-447, 1995.
DOI : 10.1049/cp:19950597

D. Tax and R. Duin, Outlier detection using classifier instability, Advances in Pattern Recognition, pp.251-256, 1999.
DOI : 10.1007/BFb0033283

R. Udry, The national longitudinal of adolescent health: waves 1 and 2, 1994.

D. Yeung and C. Chow, Parzen window network intrusion detectors, Proc. of International Conference on Pattern Recognition, 2002.