J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, Sequential pattern mining using a bitmap representation, KDD, pp.429-435, 2002.

F. Berzal, J. Cubero, and N. Marín, Anomalous association rules, IEEE ICDM Workshop Alternative Techniques for Data Mining and Knowledge Discovery, 2004.

E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk et al., Finding interesting associations without support pruning, IEEE Trans. Knowl. Data Eng, vol.13, issue.1, pp.64-78, 2001.

L. Geng and H. J. Hamilton, Interestingness measures for data mining: A survey, ACM Comput. Surv, vol.38, issue.3, 2006.

J. Han and Y. Fu, Discovery of multiple-level association rules from large databases, VLDB, pp.420-431, 1995.

F. Hussain, H. Liu, E. Suzuki, and H. Lu, Exception rule mining with a relative interestingness measure, PAKDD, pp.86-97, 2000.

B. Liu, W. Hsu, S. Chen, and Y. Ma, Analyzing the subjective interestingness of association rules, IEEE Intelligent Systems, vol.15, issue.5, pp.47-55, 2000.

S. Ma and J. L. Hellerstein, Mining mutually dependent patterns, ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining, pp.409-416, 2001.

J. Pei, J. Han, B. Mortazavi-asl, J. Wang, H. Pinto et al., Mining sequential patterns by pattern-growth: The prefixspan approach, IEEE Transactions on Knowledge and Data Engineering, vol.16, issue.10, 2004.

H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen et al., Multi-dimensional sequential pattern mining, CIKM2001, pp.81-88, 2001.

M. Plantevit, Y. W. Choong, A. Laurent, D. Laurent, and M. Teisseire, M 2 SP: Mining sequential patterns among several dimensions, PKDD, 2005.
URL : https://hal.archives-ouvertes.fr/lirmm-00106087

S. Sahar, Interestingness via what is not interesting, Knowledge Discovery and Data Mining, pp.332-336, 1999.

A. Silberschatz and A. Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Trans. Knowl. Data Eng, vol.8, issue.6, 1996.

P. Smyth and R. M. Goodman, An information theoretic approach to rule induction from databases, IEEE Trans. Knowl. Data Eng, vol.4, issue.4, pp.301-316, 1992.

R. Srikant, Q. Vu, and R. , Mining association rules with item constraints, KDD, pp.67-73, 1997.

E. Suzuki, Scheduled discovery of exception rules, DS '99: Proceedings of the Second International Conference on Discovery Science, pp.184-195, 1999.

E. Suzuki, In pursuit of interesting patterns with undirected discovery of exception rules, Progress in Discovery Science, pp.504-517, 2002.

E. Suzuki, Undirected discovery of interesting exception rules, vol.IJPRAI, pp.1065-1086, 2002.

E. Suzuki and M. Shimura, Exceptional knowledge discovery in databases based on information theory, KDD, pp.275-278, 1996.

E. Suzuki and J. M. Zytkow, Unified algorithm for undirected discovery of exception rules, International Journal of Intelligent Systems, vol.20, issue.7, pp.673-691, 2005.

P. Tan, V. Kumar, and J. Srivastava, Selecting the right objective measure for association analysis, Inf. Syst, vol.29, issue.4, pp.293-313, 2004.

J. Yang, W. Wang, and P. S. Yu, Mining surprising periodic patterns, Data Min. Knowl. Discov, vol.9, issue.2, pp.189-216, 2004.

C. Yu and Y. Chen, Mining sequential patterns from multidimensional sequence data, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.1, pp.136-140, 2005.

M. J. Zaki, Spade: An efficient algorithm for mining frequent sequences, Machine Learning, vol.42, pp.31-60, 2001.

N. Zhong, Y. Yao, and M. Ohshima, Peculiarity oriented multidatabase mining, IEEE Trans. Knowl. Data Eng, vol.15, issue.4, pp.952-960, 2003.