T. L. Taylor, Raising the Stakes : E-Sports and the Professionalization of Computer Gaming, 2012.

G. Cheung and J. Huang, Starcraft from the stands, Proceedings of the 2011 annual conference on Human factors in computing systems, CHI '11, pp.763-772, 2011.
DOI : 10.1145/1978942.1979053

M. Kaytoue, A. Silva, L. Cerf, W. M. Jr, and C. Ra¨?ssira¨?ssi, Watch me playing, i am a professional, Proceedings of the 21st international conference companion on World Wide Web, WWW '12 Companion, pp.1181-1188, 2012.
DOI : 10.1145/2187980.2188259

URL : https://hal.archives-ouvertes.fr/hal-00697150

A. and V. Eschen, Machine learning and data mining in call of duty, European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), ser, 2014.

M. A. Ahmad, B. Keegan, J. Srivastava, D. Williams, and N. S. Contractor, Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGs, 2009 International Conference on Computational Science and Engineering, pp.340-345, 2009.
DOI : 10.1109/CSE.2009.307

S. Ontañón, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill et al., A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft, IEEE Transactions on Computational Intelligence and AI in Games, vol.5, issue.4, pp.293-311, 2013.
DOI : 10.1109/TCIAIG.2013.2286295

O. Missura and T. Gärtner, Predicting dynamic difficulty, Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems, pp.2007-2015, 2011.

U. M. Fayyad, G. Piatetsky-shapiro, and P. Smyth, The KDD process for extracting useful knowledge from volumes of data, Communications of the ACM, vol.39, issue.11, pp.27-34, 1996.
DOI : 10.1145/240455.240464

C. C. Aggarwal, Data Mining -The Textbook, 2015.

A. Giacometti, D. H. Li, P. Marcel, and A. Soulet, 20 years of pattern mining, ACM SIGKDD Explorations Newsletter, vol.15, issue.1, pp.41-50, 2013.
DOI : 10.1145/2594473.2594480

URL : https://hal.archives-ouvertes.fr/hal-01171955

J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen et al., Prefixspan: Mining sequential patterns by prefix-projected growth, Proceedings of the 17th International Conference on Data Engineering, pp.215-224, 2001.

G. Dong and J. Li, Efficient mining of emerging patterns, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.43-52, 1999.
DOI : 10.1145/312129.312191

P. K. Novak, N. Lavrac, and G. I. Webb, Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, Journal of Machine Learning Research, vol.10, pp.377-403, 2009.

M. Plantevit and B. Crémilleux, Condensed Representation of Sequential Patterns According to Frequency-Based Measures, Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, pp.155-166, 2009.
DOI : 10.1109/TKDE.2007.1043

URL : https://hal.archives-ouvertes.fr/hal-01011587

G. Bosc, M. Kaytoue, C. Ra¨?ssira¨?ssi, J. Boulicaut, and P. Tan, Balancespan Available: https://github.com/ guillaume-bosc/BalanceSpan Mining dominant patterns in the sky, 11th IEEE International Conference on Data Mining, ICDM 2011, pp.655-664, 2011.

M. Van-leeuwen, Interactive data exploration using pattern mining, " in Interactive Knowledge Discovery and Data Mining in Biomedical Informatics -State-of-the-Art and Future Challenges, pp.169-182, 2014.

M. García-borroto, J. F. Trinidad, and J. A. Carrasco-ochoa, A survey of emerging patterns for supervised classification, Artificial Intelligence Review, vol.17, issue.4, pp.705-721, 2014.
DOI : 10.1007/s10462-012-9355-x

S. O. Kuznetsov, Galois Connections in Data Analysis: Contributions from the Soviet Era and Modern Russian Research, Formal Concept Analysis, Foundations and Applications, pp.196-225, 2005.
DOI : 10.1007/11528784_11

T. M. Mitchell, Machine learning, ser. McGraw Hill series in computer science, 1997.

S. D. Bay and M. J. Pazzani, Detecting group differences: Mining contrast sets, Data Mining and Knowledge Discovery, vol.5, issue.3, pp.213-246, 2001.
DOI : 10.1023/A:1011429418057

F. Herrera, C. J. Carmona, P. González, and M. J. , An overview on subgroup discovery: foundations and applications, Knowledge and Information Systems, vol.77, issue.1, pp.495-525, 2011.
DOI : 10.1007/s10115-010-0356-2

D. W. Aha, M. Molineaux, and M. J. Ponsen, Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game, Case-Based Reasoning, Research and Development, 6th International Conference, on Case-Based Reasoning, pp.5-20, 2005.
DOI : 10.1007/11536406_4

B. G. Weber and M. Mateas, Case-based reasoning for build order in real-time strategy games, Proceedings of the Fifth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp.106-111, 2009.

M. Stanescu and M. Certicky, Predicting opponent's production in real-time strategy games with answer set programming Computational Intelligence and AI in Games, IEEE Transactions on, vol.PP, issue.99, pp.1-1, 2014.

E. W. Dereszynski, J. Hostetler, A. Fern, T. G. Dietterich, T. Hoang et al., Learning probabilistic behavior models in real-time strategy games, Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2011, pp.20-25, 2011.

B. G. Weber and M. Mateas, A data mining approach to strategy prediction, 2009 IEEE Symposium on Computational Intelligence and Games, pp.140-147, 2009.
DOI : 10.1109/CIG.2009.5286483

G. Synnaeve and P. Bessì-ere, A Bayesian model for opening prediction in RTS games with application to StarCraft, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11), pp.281-288, 2011.
DOI : 10.1109/CIG.2011.6032018

URL : https://hal.archives-ouvertes.fr/hal-00607277

M. Leece and A. Jhala, Sequential pattern mining in starcraft: Brood war for short and long-term goals, Proceedings of the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment , AIIDE 2014, pp.281-288, 2014.