L. Bordeaux, M. Cadoli, and T. Mancini, CSP properties for quantified constraints : Definitions and complexity, National Conference on Artificial Intelligence, pp.360-365

B. Bringmann and A. Zimmermann, The Chosen Few: On Identifying Valuable Patterns, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.63-72, 2007.
DOI : 10.1109/ICDM.2007.85

B. Bringmann and A. Zimmermann, One in a million: picking the right patterns, Knowledge and Information Systems, vol.6, issue.3, pp.61-81, 2009.
DOI : 10.1007/s10115-008-0136-4

B. Crémilleux and A. Soulet, Discovering Knowledge from Local Patterns with Global Constraints, ICCSA (2), pp.1242-1257, 2008.
DOI : 10.1007/978-3-540-69848-7_99

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

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

A. Giacometti, E. Khanjari-miyaneh, P. Marcel, and A. Soulet, A Framework for Pattern-Based Global Models, IDEAL, pp.433-440, 2009.
DOI : 10.1007/978-3-540-75549-4_12

T. Guns, S. Nijssen, and L. De-raedt, k-Pattern Set Mining under Constraints, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.2, 2011.
DOI : 10.1109/TKDE.2011.204

P. Van-hentenryck, Y. Deville, and C. Teng, A generic arc-consistency algorithm and its specializations, Artificial Intelligence, vol.57, issue.2-3, pp.291-321, 1992.
DOI : 10.1016/0004-3702(92)90020-X

M. Khiari, P. Boizumault, and B. Crémilleux, Extraction de motifs n-aires utilisant la PPC, 6- ` emes Journées Francophones de Programmation par Contraintes (JFPC'10), pp.167-176, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00520311

M. Khiari, P. Boizumault, and B. Crémilleux, Constraint Programming for Mining n-ary Patterns, Lecture Notes in Computer Science, vol.6308, pp.552-567, 2010.
DOI : 10.1007/978-3-642-15396-9_44

URL : https://hal.archives-ouvertes.fr/hal-01016652

J. Kléma, S. Blachon, A. Soulet, B. Crémilleux, O. Gandrillon et al., Constraint-based knowledge discovery from sage data From local patterns to global models : The lego approach to data mining, Int. Workshop LeGo co-located with ECML/PKDD'08, pp.1-16, 2008.

J. Arno, E. K. Knobbe, and . Ho, Pattern teams, PKDD, pp.577-584, 2006.

V. S. Laks, R. T. Lakshmanan, J. Ng, A. Han, and . Pang, Optimization of constrained frequent set queries with 2-variable constraints, SIGMOD Conference, pp.157-168, 1999.

J. Métivier, P. Boizumault, B. Crémilleux, M. Khiari, and S. Loudni, A Constraint-Based Language for Declarative Pattern Discovery, 2011 IEEE 11th International Conference on Data Mining Workshops, pp.1-7, 2012.
DOI : 10.1109/ICDMW.2011.11

R. T. Ng, V. S. Lakshmanan, J. Han, and A. Pang, Exploratory mining and pruning optimizations of constrained associations rules, proceedings of ACM SIGMOD'98, pp.13-24

S. Nijssen and T. Guns, Integrating Constraint Programming and Itemset Mining, ECML/PKDD, pp.467-482, 2010.
DOI : 10.1007/978-3-642-15883-4_30

L. De, R. , and A. Zimmermann, Constraint-based pattern set mining, SDM. SIAM, 2007.

E. Suzuki, UNDIRECTED DISCOVERY OF INTERESTING EXCEPTION RULES, International Journal of Pattern Recognition and Artificial Intelligence, vol.16, issue.08, pp.1065-1086, 2002.
DOI : 10.1142/S0218001402002155

J. Vautard, Modélisation et résolution de probì emes de décision et d'optimisation hiérarchiques en utilisant des contraintes quantifiées. These, 2010.

D. Xin, H. Cheng, X. Yan, and J. Han, Extracting redundancy-aware top-k patterns, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.444-453, 2006.
DOI : 10.1145/1150402.1150452

X. Yin and J. Han, CPAR: Classification based on Predictive Association Rules, proceedings of the 2003 SIAM Int. Conf. on Data Mining (SDM'03), 2003.
DOI : 10.1137/1.9781611972733.40