M. David, . Blei, I. Michael, and . Jordan, Variational inference for dirichlet process mixtures, Bayesian analysis, vol.1, issue.1, pp.121-143, 2006.

A. Miguel and . Carreira-perpinan, Gaussian mean-shift is an em algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, issue.5, pp.767-776, 2007.

D. Andrew-clifton, S. Hugueny, and L. Tarassenko, Novelty Detection with Multivariate Extreme Value Statistics, Journal of Signal Processing Systems, vol.259, issue.2, pp.371-389, 2011.
DOI : 10.1006/jsvi.2002.5168

A. Paul, S. Crook, G. Marsland, U. Hayes, and . Nehmzow, A tale of two filters-on-line novelty detection, Robotics and Automation Proceedings. ICRA'02. IEEE International Conference on, pp.3894-3899, 2002.

T. Fawcett and F. Provost, Adaptive fraud detection, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.291-316, 1997.
DOI : 10.1023/A:1009700419189

J. Gehrke, V. Ganti, R. Ramakrishnan, and W. Loh, BOAT---optimistic decision tree construction, ACM SIGMOD Record, vol.28, issue.2, pp.169-180, 1999.
DOI : 10.1145/304181.304197

URL : http://www.cs.cornell.edu/johannes/papers/boat-sigmod99.ps

S. Ghosh, L. Douglas, and . Reilly, Credit card fraud detection with a neural-network, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94, pp.621-630, 1994.
DOI : 10.1109/HICSS.1994.323314

S. Guha, R. Rastogi, and K. Shim, Rock: A robust clustering algorithm for categorical attributes, Data Engineering Proceedings., 15th International Conference on, pp.512-521, 1999.
DOI : 10.1016/s0306-4379(00)00022-3

URL : http://www.cs.uiuc.edu/class/fa05/cs591han/papers/guha99.pdf

M. Colin, P. Heilig, A. Colbath, M. Iron, M. Zitkova et al., Edifact implementation guide. Passenger and airport data interchange standards, 2013.

D. Kim, P. Kang, and S. Cho, Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing, Expert Systems with Applications, vol.39, issue.4, pp.4075-4083, 2012.
DOI : 10.1016/j.eswa.2011.09.088

H. Kim, S. Pang, H. Je, D. Kim, and S. Y. Bang, Constructing support vector machine ensemble, Pattern Recognition, vol.36, issue.12, pp.2757-2767, 2003.
DOI : 10.1016/S0031-3203(03)00175-4

R. D. Lawrence, G. S. Almasi, and H. E. Rushmeier, A scalable parallel algorithm for self-organizing maps with applications to sparse data mining problems, Data Mining and Knowledge Discovery, vol.3, issue.2, pp.171-195, 1999.
DOI : 10.1023/A:1009817804059

C. Leys, C. Ley, O. Klein, P. Bernard, and L. Licata, Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, Journal of Experimental Social Psychology, vol.49, issue.4, pp.764-766, 2013.
DOI : 10.1016/j.jesp.2013.03.013

J. Geoffrey, . Mclachlan, E. Kaye, and . Basford, Mixture models. inference and applications to clustering. Statistics: Textbooks and Monographs, 1988.

A. Muñoz and J. Muruzábal, Self-organizing maps for outlier detection, Neurocomputing, vol.18, issue.1-3, pp.33-60, 1998.
DOI : 10.1016/S0925-2312(97)00068-4

I. Murray, D. Mackay, P. Ryan, and . Adams, The gaussian process density sampler, Advances in Neural Information Processing Systems, pp.9-16, 2009.

V. Torra, The weighted OWA operator, International Journal of Intelligent Systems, vol.12, issue.2, pp.153-166, 1997.
DOI : 10.1002/(SICI)1098-111X(199702)12:2<153::AID-INT3>3.0.CO;2-P

Z. Xu, An overview of methods for determining OWA weights, International Journal of Intelligent Systems, vol.34, issue.8, pp.843-865, 2005.
DOI : 10.1016/j.ins.2004.02.003

Y. Dit, C. Yeung, and . Chow, Parzen-window network intrusion detectors, Pattern Recognition Proceedings. 16th International Conference on, pp.385-388, 2002.

Y. Dit, Y. Yeung, and . Ding, Host-based intrusion detection using dynamic and static behavioral models, Pattern recognition, vol.36, issue.1, pp.229-243, 2003.