R. Agrawal and R. Srikant, Mining sequential patterns, International Conference on Data Engineering (ICDE), pp.3-14, 1995.

J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, Sequential pattern mining using a bitmap representation, International Conference on Knowledge Discovery and Data Mining (KDD), pp.429-435, 2002.

K. Beyer and R. Ramakrishnan, Bottom-up computation of sparse and iceberg cube, International Conference on Management of Data (SIGMOD), pp.359-370, 1999.

F. Bonchi and C. Lucchese, On condensed representations of constrained frequent patterns, Information Systems, vol.9, pp.180-201, 2006.

J. Boulicaut, A. Bykowski, and C. Rigotti, Free-sets: A condensed representation of boolean data for the approximation of frequency queries, Data Mining and Knowledge Discovery, vol.7, pp.5-22, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01503814

D. Burdick, M. Calimlim, and J. Gehrke, MAFIA: A maximal frequent itemset algorithm for transactional databases, International Conference on Data Engineering (ICDE), pp.443-452, 2001.

T. Calders and B. Goethals, Mining all non-derivable frequent itemsets, Principles and Practice of Knowledge Discovery in Databases (PKDD), vol.2431, pp.74-85, 2002.

T. Calders, C. Rigotti, and J. Boulicaut, A survey on condensed representations for frequent ACM Journal Name, p.20, 2006.

, Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, vol.3848, pp.64-80

D. Chiu, Y. Wu, and A. L. Chen, An efficient algorithm for mining frequent sequences by a new strategy without support counting, International Conference on Data Engineering (ICDE), pp.375-386, 2004.

S. De-amo, D. A. Furtado, A. Giacometti, and D. Laurent, An apriori-based approach for first-order temporal pattern mining, Simpósio Brasileiro de Bancos de Dados, pp.48-62, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00661551

T. Dietterich and R. Michalski, Discovering patterns in sequences of events, Artificial Intelligence, vol.25, pp.187-232, 1985.

J. Han and Y. Fu, Mining multiple-level association rules in large databases, IEEE Transactions on Knowledge and Data Engineering, vol.11, pp.798-804, 1999.

W. Inmon, Building the Data Warehouse, 2003.

H. Mannila and H. Toivonen, Multiple uses of frequent sets and condensed representations, International Conference on Knowledge Discovery and Data Mining (KDD), pp.189-194, 1996.

H. Mannila, H. Toivonen, and A. Verkamo, Discovering frequent episodes in sequences, International Conference on Knowledge Discovery and Data Mining (KDD), pp.210-215, 1995.

F. Masseglia, F. Cathala, and P. Poncelet, The PSP approach for mining sequential patterns, Principles and Practice of Knowledge Discovery in Databases (PKDD), vol.1510, pp.176-184, 1998.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering frequent closed itemsets for association rules, International Conference on Database Theory (ICDT), vol.1540, pp.398-416, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00467747

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Efficient mining of association rules using closed itemset lattices, Information Systems, vol.24, pp.25-46, 1999.

J. Pei, J. Han, and R. Mao, Closet: An efficient algorithm for mining frequent closed itemsets, SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp.21-30, 2000.

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, pp.1424-1440, 2004.

H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen et al., Multi-dimensional sequential pattern mining, International Conference on Information and Knowledge Management (CIKM), pp.81-88, 2001.

M. Plantevit, Y. W. Choong, A. Laurent, D. Laurent, and M. Teisseire, M2SP: Mining sequential patterns among several dimensions, Principles and Practice of Knowledge Discovery in Databases (PKDD). LNAI, vol.3721, pp.205-216, 2005.
URL : https://hal.archives-ouvertes.fr/lirmm-00106087

M. Plantevit, A. Laurent, and M. Teisseire, HYPE: Mining hierarchical sequential patterns, International Workshop on Data Warehousing and OLAP (DOLAP), pp.19-26, 2006.
URL : https://hal.archives-ouvertes.fr/lirmm-00102862

S. Rashad, M. M. Kantardzic, and A. Kumar, MSP-CACRR: Multidimensional Sequential Patterns Based Call Admission Control and Resource Reservation for Next-Generation Wireless Cellular Networks, Symposium on Computational Intelligence and Data Mining (CIDM), pp.552-559, 2007.

R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Extending Data Base Technology (EDBT), vol.1057, pp.3-17, 1996.

J. Stefanowski, Algorithms for context based sequential pattern mining, Fundamenta Informaticae, vol.76, pp.495-510, 2007.

J. Stefanowski and R. Ziembinski, Mining context based sequential patterns, Atlantic Web Intelligence Conference (AWIC), vol.3528, pp.401-407, 2005.

Z. Yang, M. Kitsuregawa, and Y. Wang, Paid: Mining sequential patterns by passed item deduction in large databases, International Database Engineering and Applications Symposium (IDEAS), pp.113-120, 2006.

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

M. J. Zaki, SPADE: an efficient algorithm for mining frequent sequences, Machine Learning Journal, Special issue on Unsupervised Learning, vol.42, issue.2, pp.31-60, 2001.

M. J. Zaki, Mining non-redundant association rules, Data Mining and Knowledge Discovery, vol.9, pp.223-248, 2004.

M. J. Zaki and C. Hsiao, CHARM: an efficient algorithm for closed itemset mining, SIAM International Conference on Data Mining (SDM). SIAM, 2002.

C. Zhang, K. Hu, Z. Chen, L. Chen, and Y. Dong, ApproxMGMSP: A scalable method of mining approximate multidimensional sequential patterns on distributed system, International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol.2, pp.730-734, 2007.