C. C. Aggarwal and P. S. Yu, Outlier detection for high dimensional data, ACM SIGMOD Record, vol.30, issue.2, pp.37-46, 2001.
DOI : 10.1145/376284.375668

R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules between Sets of Items in Large Databases, Proceedings of the 1993 ACM SIGMOD Conf, pp.207-216, 1993.

E. Aleskerov, B. Freisleben, and B. Rao, CARDWATCH: a neural network based database mining system for credit card fraud detection, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 1997.
DOI : 10.1109/CIFER.1997.618940

N. Billor, A. S. Hadi, and P. F. Velleman, BACON: blocked adaptive computationally efficient outlier nominators, Computational Statistics & Data Analysis, vol.34, issue.3, 2000.
DOI : 10.1016/S0167-9473(99)00101-2

M. M. Breunig, H. Kriegel, R. T. Ng, and J. Sander, LOF, ACM SIGMOD Record, vol.29, issue.2, pp.93-104, 2000.
DOI : 10.1145/335191.335388

W. Chimphlee, A. H. Abdullah, M. N. Sap, and S. Chimphlee, Unsupervised anomaly detection with unlabeled data using clustering, International conference on information and communication technology, 2005.

C. K. Chui, An Introduction to Wavelets, Computers in Physics, vol.6, issue.6, 1992.
DOI : 10.1063/1.4823126

I. Daubechies, Ten lectures on wavelets, Society for Industrial and Applied Mathematics, 1992.

L. Ertoz, E. Eilertson, A. Lazarevic, P. Tan, V. Kumar et al., Minds -minnesota intrusion detection system. Data Mining -Next Generation Challenges and Future Directions, 2004.

E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, A Geometric Framework for Unsupervised Anomaly Detection, 2002.
DOI : 10.1007/978-1-4615-0953-0_4

H. Fan, O. R. Zaiane, A. Foss, and J. Wu, A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data, PAKDD, 2006.
DOI : 10.1007/11731139_66

R. Fujimaki, T. Yairi, and K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, 2005.
DOI : 10.1145/1081870.1081917

Z. He, X. Xu, and S. Deng, Discovering cluster-based local outliers, Pattern Recognition Letters, vol.24, issue.9-10, 2003.
DOI : 10.1016/S0167-8655(03)00003-5

M. F. Jaing, S. S. Tseng, and C. M. Su, Two-phase clustering process for outliers detection, Pattern Recognition Letters, vol.22, issue.6-7, pp.6-7691, 2001.
DOI : 10.1016/S0167-8655(00)00131-8

W. Jin, A. K. Tung, and J. Han, Mining top-n local outliers in large databases, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '01, pp.293-298, 2001.
DOI : 10.1145/502512.502554

J. , J. Oldmeadow, S. Ravinutala, and C. Leckie, Adaptive clustering for network intrusion detection, 8th PAKDD, pp.255-259, 2004.

E. M. Knorr and R. T. Ng, Algorithms for mining distance-based outliers in large datasets, 24rd International Conference on Very Large Data Bases, pp.392-403, 1998.

H. Kum, J. Pei, W. Wang, and D. Duncan, ApproxMAP: Approximate Mining of Consensus Sequential Patterns, Proceedings of SIAM Int. Conf. on Data Mining, 2003.
DOI : 10.1137/1.9781611972733.36

R. Kwitt and U. Hofmann, Unsupervised Anomaly Detection in Network Traffic by Means of Robust PCA, 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07), 2007.
DOI : 10.1109/ICCGI.2007.62

W. Lee and D. Xiang, Information-theoretic measures for anomaly detection, IEEE Symposium on Security and Privacy, 2001.

T. Li, Q. Li, S. Zhu, and M. Ogihara, A survey on wavelet applications in data mining, ACM SIGKDD Explorations Newsletter, vol.4, issue.2, pp.49-68, 2002.
DOI : 10.1145/772862.772870

A. Marascu and F. Masseglia, Mining sequential patterns from data??streams: a centroid approach, Journal of Intelligent Information Systems, vol.31, issue.5, pp.291-307, 2006.
DOI : 10.1007/s10844-006-9954-6

URL : https://hal.archives-ouvertes.fr/inria-00461296

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

S. Papadimitriou, H. Kitagawa, P. B. Gibbons, and C. Faloutsos, LOCI: fast outlier detection using the local correlation integral, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), 2003.
DOI : 10.1109/ICDE.2003.1260802

L. Portnoy, E. Eskin, and S. Stolfo, Intrusion detection with unlabeled data using clustering, ACM CSS Workshop on Data Mining Applied to Security, 2001.

S. Ramaswamy, R. Rastogi, and K. Shim, Efficient algorithms for mining outliers from large data sets, ACM SIGMOD Record, vol.29, issue.2, pp.427-438, 2000.
DOI : 10.1145/335191.335437

K. Sequeira and M. Zaki, ADMIT, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.386-395, 2002.
DOI : 10.1145/775047.775103

G. Sheikholeslami, S. Chatterjee, and A. Zhang, Wavecluster: A multi-resolution clustering approach for very large spatial databases, VLDB '98: Proceedings of the 24rd International Conference on Very Large Data Bases, pp.428-439, 1998.

E. J. Stollnitz, T. D. Derose, and D. H. Salesin, Wavelets for computer graphics: a primer.1, IEEE Computer Graphics and Applications, vol.15, issue.3, pp.76-84, 1995.
DOI : 10.1109/38.376616

R. K. Young, Wavelet Theory and Its Applications, 1995.
DOI : 10.1007/978-1-4615-3584-3

D. Yu, G. Sheikholeslami, and A. Zhang, FindOut : Finding Outliers in Very Large Datasets, Knowledge and Information Systems, vol.4, issue.4, pp.387-412, 2002.
DOI : 10.1007/s101150200013

S. Zhong, T. M. Khoshgoftaar, and N. Seliya, CLUSTERING-BASED NETWORK INTRUSION DETECTION, International Journal of Reliability, Quality and Safety Engineering, vol.14, issue.02, 2007.
DOI : 10.1142/S0218539307002568