Y. S. Koh and S. D. Ravana, Unsupervised Rare Pattern Mining, ACM Transactions on Knowledge Discovery from Data, vol.10, issue.4, p.129, 2016.
DOI : 10.1016/S0164-1212(02)00128-0

Z. Hu, H. Wang, J. Zhu, M. Li, Y. Qiao et al., Discovery of Rare Sequential Topic Patterns in Document Stream, Proceedings of the SIAM International Conference on Data Mining, p.533541, 2014.
DOI : 10.1137/1.9781611973440.61

L. Szathmary, Finding minimal rare itemsets with an extended version of the Apriori algorithm, Proceedings of the 9th International Conference on Applied Informatics, Volume 1, p.8592, 2014.
DOI : 10.14794/ICAI.9.2014.1.85

S. Muggleton and L. De-raedt, Inductive Logic Programming: Theory and methods, The Journal of Logic Programming, vol.19, issue.20, p.679, 1994.
DOI : 10.1016/0743-1066(94)90035-3
URL : http://doi.org/10.1016/0743-1066(94)90035-3

L. De-raedt, T. Guns, and S. Nijssen, Constraint programming for itemset mining, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, p.204212, 2008.
DOI : 10.1145/1401890.1401919

M. Sebag and C. Rouveirol, Constraint inductive logic programming In: Advances in ILP, p.277294, 1996.

D. Corapi, A. Russo, and E. Lupu, Inductive Logic Programming in Answer Set Programming, International Conference on Inductive Logic Programming, p.9197, 2011.
DOI : 10.1007/978-3-642-31951-8_12

S. Jabbour, L. Sais, and Y. Salhi, Decomposition Based SAT Encodings for Itemset Mining Problems, Proceeding of the Pacic-Asia Conference Advances on Knowledge Discovery and Data Mining, p.662674, 2015.
DOI : 10.1007/978-3-319-18032-8_52

L. De-raedt, T. Guns, and S. Nijssen, Constraint programming for data mining and machine learning, Proceedings of the Twenty-Fourth AAAI Conference on Articial Intelligence (AAAI-10, p.16711675, 2010.

T. Guns, A. Dries, S. Nijssen, G. Tack, and L. D. Raedt, MiningZinc: A declarative framework for constraint-based mining, Artificial Intelligence, vol.244, pp.244-629, 2017.
DOI : 10.1016/j.artint.2015.09.007

M. Gebser, T. Guyet, R. Quiniou, J. Romero, and T. Schaub, Knowledge-based sequence mining with ASP, Proceedings of the Twenty-Fifth International Joint Conference on Articial Intelligence (IJCAI, p.14971504, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01327363

T. Janhunen and I. Niemelä, The Answer Set Programming Paradigm, AI Magazine, vol.37, issue.3, p.1324, 2016.
DOI : 10.1609/aimag.v37i3.2671

M. Gebser, R. Kaminski, B. Kaufmann, M. Ostrowski, T. Schaub et al., Potassco: The Potsdam answer set solving collection, AI Communications, vol.24, issue.2, p.107124, 2011.
DOI : 10.1007/978-3-642-02846-5_22
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.1611

L. Szathmary, P. Valtchev, and A. Napoli, Generating rare association rules using the minimal rare itemsets family, International Journal on Software and Informatics, vol.4, issue.3, p.219238, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00551503

B. Negrevergne and T. Guns, Constraint-Based Sequence Mining Using Constraint Programming, Proceedings of the International Conference on Integration of AI and OR Techniques in Constraint Programming, p.288305, 2015.
DOI : 10.1007/978-3-319-18008-3_20
URL : http://arxiv.org/abs/1501.01178