A. Amelio and C. Pizzuti, Analyzing voting behavior in italian parliament: Group cohesion and evolution, International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp.140-146, 2012.

S. Amer-yahia, S. Kleisarchaki, N. K. Kolloju, L. Lakshmanan, and R. H. Zamar, Exploring rated datasets with rating maps, Proceedings of the 26th International Conference on World Wide Web, pp.1411-1419, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02000539

M. Atzmueller, Subgroup discovery, Wiley Interdiscip Rev Data Min Knowl Discov, vol.5, issue.1, pp.35-49, 2015.

M. Atzmüller and F. Puppe, Sd-map -A fast algorithm for exhaustive subgroup discovery, Knowledge Discovery in Databases: PKDD, p.10, 2006.

, European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.6-17, 2006.

S. D. Bay and M. J. Pazzani, Detecting group differences: Mining contrast sets, Data Min Knowl Discov, vol.5, issue.3, pp.213-246, 2001.

A. Belfodil, S. Cazalens, P. Lamarre, and M. Plantevit, Flash points: Discovering exceptional pairwise behaviors in vote or rating data, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2017, pp.442-458, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01587041

A. Belfodil, S. Cazalens, P. Lamarre, and M. Plantevit, Identifying exceptional (dis)agreement between groups, LIRIS UMR CNRS 5205, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02018813

A. A. Bendimerad, M. Plantevit, and C. Robardet, Unsupervised exceptional attributed sub-graph mining in urban data, IEEE 16th International Conference on Data Mining, ICDM 2016, pp.21-30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01430622

A. A. Bendimerad, R. Cazabet, M. Plantevit, and C. Robardet, Contextual subgraph discovery with mobility models, Complex Networks & Their Applications VI -Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), COM-PLEX NETWORKS 2017, pp.477-489, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01625068

M. Boley, T. Gärtner, and H. Grosskreutz, Formal concept sampling for counting and threshold-free local pattern mining, Proceedings of the SIAM International Conference on Data Mining, SDM 2010, pp.177-188, 2010.

M. Boley, T. Horváth, A. Poigné, and S. Wrobel, Listing closed sets of strongly accessible set systems with applications to data mining, Theor Comput Sci, vol.411, issue.3, pp.691-700, 2010.

M. Boley, C. Lucchese, D. Paurat, and T. Gärtner, Direct local pattern sampling by efficient two-step random procedures, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.582-590, 2011.

M. Boley, S. Moens, and T. Gärtner, Linear space direct pattern sampling using coupling from the past, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.69-77, 2012.

G. Bosc, J. Golebiowski, M. Bensafi, C. Robardet, M. Plantevit et al., Local subgroup discovery for eliciting and understanding new structure-odor relationships, Discovery Science -19th International Conference, pp.19-34, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01346660

G. Bosc, J. Boulicaut, C. Raïssi, and M. Kaytoue, Anytime discovery of a diverse set of patterns with monte carlo tree search, Data Min Knowl Discov, vol.32, issue.3, pp.604-650, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01662857

Y. Charalabidis, C. Alexopoulos, and E. Loukis, A taxonomy of open government data research areas and topics, Journal of Organizational Computing and Electronic Commerce, vol.26, issue.1-2, pp.41-63, 2016.

I. Csisz, Information-type measures of difference of probability distributions and indirect observations, Studia Sci Math Hungar, vol.2, pp.299-318, 1967.

M. Das, S. Amer-yahia, G. Das, and C. Yu, MRI: meaningful interpretations of collaborative ratings, PVLDB, vol.4, issue.11, pp.1063-1074, 2011.

G. Dong and J. Li, Efficient mining of emerging patterns: Discovering trends and differences, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.43-52, 1999.

L. Downar and W. Duivesteijn, Exceptionally monotone models-the rank correlation model class for exceptional model mining. Knowledge and Information Systems, vol.51, pp.369-394, 2017.

W. Duivesteijn, A. J. Knobbe, A. Feelders, and M. Van-leeuwen, Subgroup discovery meets bayesian networks -an exceptional model mining approach, The 10th IEEE International Conference on Data Mining, pp.158-167, 2010.

W. Duivesteijn, A. Feelders, and A. J. Knobbe, Exceptional model miningsupervised descriptive local pattern mining with complex target concepts, Data Min Knowl Discov, vol.30, issue.1, pp.47-98, 2016.

V. Dzyuba, M. Van-leeuwen, and L. D. Raedt, Flexible constrained sampling with guarantees for pattern mining, Data Min Knowl Discov, vol.31, issue.5, pp.1266-1293, 2017.

V. Etter, J. Herzen, M. Grossglauser, and P. Thiran, Mining democracy, Proceedings of the second ACM conference on Online social networks, pp.1-12, 2014.

J. Fürnkranz, D. Gamberger, and N. Lavrac, Foundations of Rule Learning. Cognitive Technologies, 2012.

B. Ganter and S. O. Kuznetsov, Conceptual Structures: Broadening the Base, 9th International Conference on Conceptual Structures, vol.2120, pp.129-142, 2001.

B. Ganter and R. Wille, Formal Concept Analysis -Mathematical Foundations, 1999.

A. Giacometti and A. Soulet, Frequent pattern outlier detection without exhaustive mining, Advances in Knowledge Discovery and Data Mining -20th Pacific-Asia Conference, pp.196-207, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01280595

H. Grosskreutz and S. Rüping, On subgroup discovery in numerical domains, Data Min Knowl Discov, vol.19, issue.2, pp.210-226, 2009.

H. Grosskreutz, S. Rüping, and S. Wrobel, Tight optimistic estimates for fast subgroup discovery, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD, pp.440-456, 2008.

H. Grosskreutz, M. Boley, and M. Krause-traudes, Subgroup discovery for election analysis: A case study in descriptive data mining, Discovery Science -13th International Conference, DS 2010, pp.57-71, 2010.

H. Grosskreutz, B. Lang, and D. Trabold, A relevance criterion for sequential patterns, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2013, pp.369-384, 2013.

F. M. Harper and J. A. Konstan, The movielens datasets: History and context, TiiS, vol.5, issue.4, p.19, 2016.

M. A. Hasan and M. J. Zaki, Output space sampling for graph patterns, PVLDB, vol.2, issue.1, pp.730-741, 2009.

A. F. Hayes and K. Krippendorff, Answering the call for a standard reliability measure for coding data, Communication methods and measures, vol.1, issue.1, pp.77-89, 2007.

F. Herrera, C. J. Carmona, P. González, and M. J. Del-jesús, An overview on subgroup discovery: foundations and applications, Knowl Inf Syst, vol.29, issue.3, pp.495-525, 2011.

S. Hix, A. Noury, and G. Roland, Power to the parties: cohesion and competition in the european parliament, British Journal of Political Science, vol.35, issue.2, pp.209-234, 1979.

A. Jakulin, Analyzing the us senate in 2003 : Similarities , networks , clusters and blocs, 2004.

D. Johnson and S. Sinanovic, Symmetrizing the kullback-leibler distance, IEEE Transactions on Information Theory, 2001.

M. Kaytoue, S. O. Kuznetsov, A. Napoli, and S. Duplessis, Mining gene expression data with pattern structures in formal concept analysis, Inf Sci, vol.181, issue.10, pp.1989-2001, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00541100

M. Kaytoue, M. Plantevit, A. Zimmermann, A. A. Bendimerad, and C. Robardet, Exceptional contextual subgraph mining, Machine Learning, vol.106, issue.8, pp.1171-1211, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01488732

W. Klösgen, Explora: A multipattern and multistrategy discovery assistant, Advances in Knowledge Discovery and Data Mining, pp.249-271, 1996.

S. O. Kuznetsov and S. A. Obiedkov, Comparing performance of algorithms for generating concept lattices, J Exp Theor Artif Intell, vol.14, issue.2-3, pp.189-216, 2002.

N. Lavrac, B. Kavsek, P. A. Flach, and L. Todorovski, Subgroup discovery with CN2-SD, Data Min Knowl Discov, vol.5, issue.2, pp.208-242, 2004.

D. Leman, A. Feelders, and A. J. Knobbe, Exceptional model mining, Machine Learning and Knowledge Discovery in Databases, pp.1-16, 2008.

F. Lemmerich and M. Becker, pysubgroup: Easy-to-use subgroup discovery in python, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2018, pp.658-662, 2018.

F. Lemmerich, M. Atzmueller, and F. Puppe, Fast exhaustive subgroup discovery with numerical target concepts, Data Min Knowl Discov, vol.30, issue.3, pp.711-762, 2016.

G. Li and M. J. Zaki, Sampling frequent and minimal boolean patterns: theory and application in classification, Data Min Knowl Discov, vol.30, issue.1, pp.181-225, 2016.

B. Liu, W. Hsu, and Y. Ma, Integrating classification and association rule mining, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pp.80-86, 1998.

S. Moens and M. Boley, Instant exceptional model mining using weighted controlled pattern sampling, Advances in Intelligent Data Analysis XIII -13th International Symposium, pp.203-214, 2014.

S. Moens and B. Goethals, Randomly sampling maximal itemsets, Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, IDEA@KDD 2013, pp.79-86, 2013.

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

B. Omidvar-tehrani, S. Amer-yahia, P. Dutot, and D. Trystram, Multiobjective group discovery on the social web, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2016, pp.296-312, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02066493

J. F. Orueta, R. Nuño-solinis, M. Mateos, I. Vergara, G. Grandes et al., Monitoring the prevalence of chronic conditions: which data should we use?, BMC health services research, vol.12, issue.1, p.365, 2012.

A. Pajala, A. Jakulin, and W. Buntine, Parliamentary group and individual voting behavior in finnish parliament in year 2003 : A group cohesion and voting similarity analysis, 2004.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering frequent closed itemsets for association rules, Database Theory -ICDT '99, 7th International Conference, pp.398-416, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00467747

E. Roddy and M. Doherty, Epidemiology of gout, Arthritis research & therapy, vol.12, issue.6, p.223, 2010.

S. Roman, Lattices and ordered sets, 2008.

C. R. De-sá, W. Duivesteijn, C. Soares, and A. J. Knobbe, Exceptional preferences mining, Discovery Science -19th International Conference, pp.3-18, 2016.

C. R. De-sá, W. Duivesteijn, P. J. Azevedo, A. M. Jorge, C. Soares et al., Discovering a taste for the unusual: exceptional models for preference mining, Machine Learning, vol.107, issue.11, pp.1775-1807, 2018.

A. Terada, M. Okada-hatakeyama, K. Tsuda, and J. Sese, Statistical significance of combinatorial regulations, Proceedings of the National Academy of Sciences, vol.110, issue.32, p.1, 2013.

J. W. Tukey, Exploratory data analysis. Addison-Wesley series in behavioral science : quantitative methods, 1977.

C. Wang and L. M. Crapo, The epidemiology of thyroid disease and implications for screening, Endocrinology and Metabolism Clinics, vol.26, issue.1, pp.189-218, 1997.

S. Wrobel, An algorithm for multi-relational discovery of subgroups, Principles of Data Mining and Knowledge Discovery, First European Symposium, PKDD '97, pp.78-87, 1997.