R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, Fast discovery of association rules, Advances in knowledge discovery and data mining, pp.307-328, 1996.

Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, Mining frequent patterns with counting inference, ACM SIGKDD Explorations Newsletter, vol.2, issue.2, pp.66-75, 2000.
DOI : 10.1145/380995.381017
URL : https://hal.archives-ouvertes.fr/hal-00467750

E. Boros, V. Gurvich, L. Khachiyan, and K. Makino, On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets, STACS '02: Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science, pp.133-141, 2002.
DOI : 10.1007/3-540-45841-7_10

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

S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, Dynamic itemset counting and implication rules for market basket data, SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp.255-264, 1997.

T. Calders, C. Rigotti, and J. Boulicaut, A Survey on Condensed Representations for Frequent Sets, Constraint-Based Mining, 2005.
DOI : 10.1007/11615576_4

E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk et al., Finding interesting associations without support pruning, IEEE Transactions on Knowledge and Data Engineering, vol.13, issue.1, pp.64-78, 2001.
DOI : 10.1109/69.908981
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.538

B. Ganter and R. Wille, Formal concept analysis: mathematical foundations, 1999.

Y. Koh and N. Rountree, Finding Sporadic Rules Using Apriori-Inverse, PAKDD '05: Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp.97-106, 2005.
DOI : 10.1007/11430919_13

Y. Koh, N. Rountree, and R. O. Keefe, Mining Interesting Imperfectly Sporadic Rules, PAKDD '06: Proceedings of the 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp.473-482, 2006.
DOI : 10.1007/11731139_55
URL : https://researchspace.auckland.ac.nz/bitstream/2292/8523/4/10.1007-s10115-007-0074-6.pdf

M. Kryszkiewicz, Concise representation of frequent patterns based on disjunction-free generators, Proceedings 2001 IEEE International Conference on Data Mining, pp.305-312, 2001.
DOI : 10.1109/ICDM.2001.989533

M. Kryszkiewicz, Concise Representations of Association Rules, Pattern Detection and Discovery, pp.92-109, 2002.
DOI : 10.1007/3-540-45728-3_8

B. Liu, W. Hsu, and Y. Ma, Mining association rules with multiple minimum supports, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.337-341, 1999.
DOI : 10.1145/312129.312274
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.8948

H. Liu, H. Lu, L. Feng, and F. Hussain, Efficient Search of Reliable Exceptions, PAKDD '99: Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, pp.194-203, 1999.
DOI : 10.1007/3-540-48912-6_27

H. Mannila and H. Toivonen, Multiple uses of frequent sets and condensed representations, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD '96), pp.189-194, 1996.

H. Mannila and H. Toivonen, Levelwise Search and Borders of Theories in Knowledge Discovery, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.241-258, 1997.
DOI : 10.1023/A:1009796218281

S. Maumus, A. Napoli, L. Szathmary, and S. Visvikis-siest, Exploitation des données de la cohorte STANISLAS par des techniques de fouille de données numériques et symboliques utilisées seules ou en combinaison, Workshop on Fouille de Données Complexes dans un Processus d'Extraction des Connaissances -EGC 2005, pp.73-76, 2005.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Efficient mining of association rules using closed itemset lattices, Information Systems, vol.24, issue.1, pp.25-46, 1999.
DOI : 10.1016/S0306-4379(99)00003-4

G. G. Piatetsky-shapiro and W. J. Piatetsky-shapiro, Knowledge discovery in databases, Chapter Discovery, Analysis, and Presentation of Strong Rules (chapter 13), pp.229-248, 1991.
DOI : 10.1145/846183.846197

G. Siest, S. Visvikis, B. Herbeth, R. Gueguen, M. Vincent-viry et al., Objectives, Design and Recruitment of a Familial and Longitudinal Cohort for Studying Gene-Environment Interactions in the Field of Cardiovascular Risk: The Stanislas Cohort, Clinical Chemistry and Laboratory Medicine, vol.36, issue.1, pp.35-42, 1998.
DOI : 10.1515/CCLM.1998.007

R. H. Sloan, K. Takata, and G. Turan, On frequent sets of Boolean matrices, Annals of Mathematics and Artificial Intelligence, vol.24, pp.1-4, 1998.

L. Szathmary, S. Maumus, P. Petronin, Y. Toussaint, and A. Napoli, Vers l'extraction de motifs rares, Extraction et gestion des connaissances, pp.499-510, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00001151

G. Weiss, Mining with rarity, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.7-19, 2004.
DOI : 10.1145/1007730.1007734

H. Yun, D. Ha, B. Hwang, and K. Ryu, Mining association rules on significant rare data using relative support, Journal of Systems and Software, vol.67, issue.3, pp.181-191, 2003.
DOI : 10.1016/S0164-1212(02)00128-0