asprin: Customizing answer set preferences without a headache, Proceedings of the Twenty-Ninth National Conference on Artificial Intelligence (AAAI'15), pp.1467-1474, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01187001
Preference-Based Frequent Pattern Mining, Proceedings of the Twentieth European Conference on Artificial Intelligence (ECAI'12), pp.56-77, 2005. ,
DOI : 10.4018/jdwm.2005100103
Maximum entropy models and subjective interestingness: an application to tiles in binary databases, Data Mining and Knowledge Discovery, vol.1, issue.1-2, pp.407-446, 2011. ,
DOI : 10.1007/s10618-010-0209-3
Improving pattern discovery relevancy by deriving constraints from expert models, Proceedings of the Twenty-first European Conference on Artificial Intelligence (ECAI'14), pp.327-332, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01151514
Clingo = ASP + control: Preliminary report, Technical Communications of the Thirtieth International Conference on Logic Programming Theory and Practice of Logic Programming):Online Supplement, 2014. ,
Classical negation in logic programs and disjunctive databases, New Generation Computing, vol.38, issue.No. 3, pp.365-385, 1991. ,
DOI : 10.1007/BF03037169
Itemset mining: A constraint programming perspective, Artificial Intelligence, vol.175, issue.12-13, pp.12-131951, 2011. ,
DOI : 10.1016/j.artint.2011.05.002
Using answer set programming for pattern mining, Actes desHuitì emes Journées d'Intelligence Artificielle Fondamentale (JIAF'14), 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01069092
PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining, Proceedings of the Twenty-first International Conference on Principles and Practice of Constraint Programming (CP'15), pp.226-243, 2015. ,
DOI : 10.1007/978-3-319-23219-5_17
UCI machine learning repository, 2013. ,
What is answer set programming?, Proceedings of the Twenty-third National Conference on Artificial Intelligence (AAAI'08), pp.1594-1597, 2008. ,
Inductive logic programming: Theory and methods [Negrevergne and Guns, 2015] B. Negrevergne and T. Guns. Constraint-based sequence mining using constraint programming [Padmanabhan and Tuzhilin, 1998] B. Padmanabhan and A. Tuzhilin. A belief-driven method for discovering unexpected patterns, Proceedings of the Twelfth International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR'15), volume 9075 of Lecture Notes in Computer Science Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98), pp.629-679, 1994. ,
Sequential Pattern Mining, Frequent Pattern Mining, pp.261-282, 2014. ,
DOI : 10.1007/978-3-319-07821-2_11
Extending and implementing the stable model semantics, Artificial Intelligence, vol.138, issue.1-2, pp.181-234, 2002. ,
DOI : 10.1016/S0004-3702(02)00187-X
SPADE: An efficient algorithm for mining frequent sequences, Machine Learning, pp.31-60, 2001. ,