Z. Xing, J. Pei, and E. Keogh, A brief survey on sequence classification, ACM SIGKDD Explorations Newsletter, vol.12, issue.1, pp.40-48, 2010.
DOI : 10.1145/1882471.1882478

M. Deshpande and G. Karypis, Evaluation of Techniques for Classifying Biological Sequences, Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, ser. PAKDD '02, pp.417-431, 2002.
DOI : 10.1007/3-540-47887-6_41

L. Wei and E. Keogh, Semi-supervised time series classification, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.748-753, 2006.
DOI : 10.1145/1150402.1150498

C. Aggarwal and C. Zhai, A Survey of Text Classification Algorithms, pp.163-222
DOI : 10.1007/978-1-4614-3223-4_6

F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, vol.34, issue.1, pp.1-47, 2002.
DOI : 10.1145/505282.505283

H. Liu and H. Motoda, Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)

N. A. Chuzhanova, A. J. Jones, and S. Margetts, Feature selection for genetic sequence classification, Bioinformatics, vol.14, issue.2, pp.139-143, 1998.
DOI : 10.1093/bioinformatics/14.2.139

C. S. Leslie, E. Eskin, and W. S. Noble, THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION, Biocomputing 2002, pp.566-575, 2002.
DOI : 10.1142/9789812799623_0053

Z. Xing, J. Pei, G. Dong, and P. Yu, Mining Sequence Classifiers for Early Prediction, SDM, pp.644-655, 2008.
DOI : 10.1137/1.9781611972788.59

R. Agrawal and R. Srikant, Mining sequential patterns, Proceedings of the Eleventh International Conference on Data Engineering, pp.3-14, 1995.
DOI : 10.1109/ICDE.1995.380415

A. Knobbe, B. Crémilleux, J. Fürnkranz, and M. Scholz, From local patterns to global models: The lego approach to data mining, From Local Patterns to Global Models: Proceedings of the ECML PKDD 2008 Workshop, pp.1-16, 2008.

M. J. Zaki, C. D. Carothers, and B. K. Szymanski, VOGUE, ACM Transactions on Knowledge Discovery from Data, vol.4, issue.1, 2010.
DOI : 10.1145/1644873.1644878

N. Lesh, M. Zaki, and M. Ogihara, Mining features for sequence classification, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.342-346, 1999.
DOI : 10.1145/312129.312275

K. Park and M. Kanehisa, Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs, Bioinformatics, vol.19, issue.13, pp.1656-1663, 2003.
DOI : 10.1093/bioinformatics/btg222

R. She, F. Chen, K. Wang, M. Ester, J. Gardy et al., Frequent-subsequence-based prediction of outer membrane proteins, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.436-445, 2003.
DOI : 10.1145/956750.956800

T. Exarchos, M. Tsipouras, C. Papaloukas, and D. Fotiadis, An optimized sequential pattern matching methodology for sequence classification, Knowledge and Information Systems, vol.11, issue.3, pp.249-264, 2009.
DOI : 10.1007/s10115-008-0146-2

V. Tseng and C. Lee, Effective temporal data classification by integrating sequential pattern mining and probabilistic induction, Expert Systems with Applications, vol.36, issue.5, pp.9524-9532, 2009.
DOI : 10.1016/j.eswa.2008.10.077

J. Li, H. Li, L. Wong, J. Pei, and G. Dong, Minimum description length principle: Generators are preferable to closed patterns, AAAI, 2006.

J. Pei, J. Han, B. Mortazavi-asl, J. Wang, H. Pinto et al., Mining sequential patterns by pattern-growth: The prefixspan approach, IEEE Trans. Knowl. Data Eng, vol.16, issue.11, pp.1424-1440, 2004.

H. Mannila, H. Toivonen, and A. Verkamo, Discovery of frequent episodes in event sequences, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.259-289, 1997.
DOI : 10.1023/A:1009748302351

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, issue.1, pp.5-22, 2003.
DOI : 10.1023/A:1021571501451

URL : https://hal.archives-ouvertes.fr/hal-01503814

C. Ra¨?ssira¨?ssi, T. Calders, and P. Poncelet, Mining conjunctive sequential patterns, Data Mining and Knowledge Discovery, vol.42, issue.1/2, pp.77-93, 2008.
DOI : 10.1007/s10618-008-0108-z

C. Gao, J. Wang, Y. He, and L. Zhou, Efficient mining of frequent sequence generators, Proceeding of the 17th international conference on World Wide Web , WWW '08, pp.1051-1052, 2008.
DOI : 10.1145/1367497.1367651

D. Lo, S. Khoo, and J. Li, Mining and Ranking Generators of Sequential Patterns, SDM, pp.553-564, 2008.
DOI : 10.1137/1.9781611972788.51

J. Wang, J. Han, and C. Li, Frequent Closed Sequence Mining without Candidate Maintenance, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.8, pp.1042-1056, 2007.
DOI : 10.1109/TKDE.2007.1043

J. Wang and J. Han, BIDE: efficient mining of frequent closed sequences, Proceedings. 20th International Conference on Data Engineering, p.79, 2004.
DOI : 10.1109/ICDE.2004.1319986

J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen et al., Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth, Proceedings of the 17th International Conference on Data Engineering, ser. ICDE '01

K. Nigam, Using maximum entropy for text classification, IJCAI-99 Workshop on Machine Learning for Information Filtering, pp.61-67, 1999.

T. Lavergne, O. Cappé, and F. Yvon, Practical very large scale CRFs, Proceedings the 48th Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, pp.504-513, 2010.

B. Crémilleux and J. Boulicaut, Simplest rules characterizing classes generated by delta-free sets, Proc. of the 22nd BCS SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence, 2002.

P. Sun, S. Chawla, and B. Arunasalam, Mining for Outliers in Sequential Databases, SDM, 2006.
DOI : 10.1137/1.9781611972764.9

A. Frank and A. Asuncion, UCI machine learning repository, 2010.

H. Mannila and H. Toivonen, Multiple uses of frequent sets and condensed representations (extended abstract), KDD, pp.189-194, 1996.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering Frequent Closed Itemsets for Association Rules, ICDT'99, pp.398-416, 1999.
DOI : 10.1007/3-540-49257-7_25

URL : https://hal.archives-ouvertes.fr/hal-00467747

T. Calders and B. Goethals, Mining All Non-derivable Frequent Itemsets, PKDD, pp.74-85, 2002.
DOI : 10.1007/3-540-45681-3_7

URL : http://arxiv.org/abs/cs/0206004

X. Yan, J. Han, and R. Afshar, CloSpan: Mining: Closed Sequential Patterns in Large Datasets, SDM, 2003.
DOI : 10.1137/1.9781611972733.15

D. Fradkin and F. Mrchen, Margin-closed frequent sequential pattern mining, Proceedings of the ACM SIGKDD Workshop on Useful Patterns, UP '10, 2010.
DOI : 10.1145/1816112.1816119

E. Baralis, S. Chiusano, R. Dutto, and L. Mantellini, Compact Representations of Sequential Classification Rules, Data Mining: Foundations and Practice, ser. Studies in Computational Intelligence, 2008.
DOI : 10.1007/978-3-540-78488-3_1

A. Soulet and F. Rioult, Efficiently depth-first minimal pattern mining Advances in Knowledge Discovery and Data Mining, ser. Lecture Notes in Computer Science, pp.28-39, 2014.

H. Grosskreutz, B. Lang, and D. Trabold, A Relevance Criterion for Sequential Patterns, ECML/PKDD, p.2013
DOI : 10.1007/978-3-642-40988-2_24

T. Joachims, Text categorization with suport vector machines: Learning with many relevant features, Proceedings of the 10th European Conference on Machine Learning, ser. ECML '98, pp.137-142, 1998.
DOI : 10.1007/bfb0026683

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.6124

R. Durbin, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, 1998.
DOI : 10.1017/CBO9780511790492

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, PROCEEDINGS OF THE IEEE, pp.257-286, 1989.
DOI : 10.1109/5.18626

M. J. Zaki, C. D. Carothers, and B. K. Szymanski, VOGUE, ACM Transactions on Knowledge Discovery from Data, vol.4, issue.1, 2010.
DOI : 10.1145/1644873.1644878

C. Watkins, Dynamic alignment kernels, Advances in Large Margin Classifiers, pp.39-50, 1999.

C. S. Leslie, E. Eskin, and W. S. Noble, THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION, Biocomputing 2002, pp.566-575, 2002.
DOI : 10.1142/9789812799623_0053