. Komiyama, For example, Webb, 2007 proposes to divide the data into Exploratory and Holdout data. Hypotheses are sought References Abudawood, Other state-of-the-art techniques follow a multi-stage procedure (Hämäläinen and Webb, 2019) to tackle the MCP. A first step constrains L to a subset of patterns, vol.5781, pp.35-50, 2007.

R. Agrawal, T. Imielinski, and A. N. Swami, Mining Association Rules between Sets of Items in Large Databases, SIGMOD Conference, pp.207-216, 1993.

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, pp.487-499, 1994.

R. Agrawal and R. Srikant, Mining sequential patterns, vol.95, pp.3-14, 1995.

A. Hasan, M. , and M. J. Zaki, Output space sampling for graph patterns, Proceedings of the VLDB Endowment 2.1, pp.730-741, 2009.

J. Alejandro, Journalism in the age of social media, Reuters Institute Fellowship Paper, p.5, 2010.

H. Alt and M. Godau, Computing the Fréchet distance between two polygonal curves, International Journal of Computational Geometry & Applications 5.01n02, pp.75-91, 1995.

A. Amelio and C. Pizzuti, Analyzing voting behavior in Italian Parliament: Group cohesion and evolution, pp.140-146, 2012.

. Amer-yahia, S. Sihem, N. Kleisarchaki, . Kumar-kolloju, V. S. Laks et al., Exploring Rated Datasets with Rating Maps, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02000539

S. Arora and B. Barak, Computational complexity: a modern approach, vol.46, 2009.

M. Atzmueller, Subgroup discovery, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5.1, vol.18, pp.35-49, 2015.

, Descriptive Community Detection, Formal Concept Analysis of Social Networks, pp.41-58, 2017.

M. Atzmueller, S. Doerfel, and F. Mitzlaff, Description-oriented community detection using exhaustive subgroup discovery, Inf. Sci, vol.329, pp.965-984, 2016.

M. Atzmueller and F. Lemmerich, VIKAMINE-open-source subgroup discovery, pattern mining, and analytics, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, vol.156, p.35, 2012.

M. Atzmüller and F. Lemmerich, Fast Subgroup Discovery for Continuous Target Concepts, ISMIS, vol.45, p.35, 2009.

M. Atzmüller and F. Puppe, SD-Map -A Fast Algorithm for Exhaustive Subgroup Discovery, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, vol.22, p.35, 2006.

S. D. Bay, J. Michael, and . Pazzani, Detecting group differences: Mining contrast sets, Data mining and knowledge discovery 5.3, vol.165, pp.213-246, 2001.

M. Becker, F. Lemmerich, P. Singer, M. Strohmaier, and A. Hotho, MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data, Data Min. Knowl. Discov. 31, vol.5, pp.1359-1390, 2017.

J. T. Behrens, Principles and procedures of exploratory data analysis, Psychological Methods, vol.2, issue.2, p.131, 1997.

A. Belfodil, S. Cazalens, P. Lamarre, and M. Plantevit, Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data, Lecture Notes in Computer Science, vol.10535, issue.2, p.70, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01587041

A. Belfodil, W. Duivesteijn, M. Plantevit, S. Cazalens, and P. Lamarre, DEvIANT: Discovering significant exceptional (dis-)agreement within groups, ECML/PKDD, vol.102, p.14, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02161309

A. Belfodil, A. Belfodil, A. Bendimerad, P. Lamarre, C. Robardet et al., FSSD -A Fast and Efficient Algorithm for Subgroup Set Discovery, vol.45, 2019.

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

A. Belfodil, A. Belfodil, and M. Kaytoue, Anytime Subgroup Discovery in Numerical Domains with Guarantees, ECML/PKDD, vol.11052, p.45, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02117627

, Mining Formal Concepts using Implications between Items, International Conference on Formal Concept Analysis, 2019.

A. Belfodil, S. O. Kuznetsov, and M. Kaytoue, Pattern Setups and Their Completions, CLA. Volume 2123. CEUR Workshop Proceedings. CEUR-WS.org, pp.243-253, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01818740

, On Pattern Setups and Pattern Multistructures, vol.25, 2019.

A. Belfodil, S. O. Kuznetsov, C. Robardet, and M. Kaytoue, Mining Convex Polygon Patterns with Formal Concept Analysis, IJCAI. ijcai.org, pp.1425-1432, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01573841

A. Bendimerad, M. Anes, C. Plantevit, and . Robardet, Unsupervised Exceptional Attributed Sub-Graph Mining in Urban Data, ICDM, pp.21-30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01430622

, Mining exceptional closed patterns in attributed graphs, Knowl. Inf. Syst, vol.56, issue.1, pp.1-25, 2018.

A. Bendimerad, R. Anes, M. Cazabet, C. Plantevit, and . Robardet, Contextual Subgraph Discovery with Mobility Models, pp.477-489, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01625068

A. Bendimerad, Mining Useful Patterns in Attributed Graphs, vol.42, 2019.
URL : https://hal.archives-ouvertes.fr/tel-02284436

A. Bendimerad, R. Cazabet, M. Plantevit, and C. Robardet, International Workshop on Complex Networks and their Applications, vol.18, p.51, 2017.

T. Bie and . De, An information theoretic framework for data mining, pp.564-572, 2011.

, Maximum entropy models and subjective interestingness: an application to tiles in binary databases, Data Min. Knowl. Discov, vol.23, issue.3, pp.407-446, 2011.

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.10, p.10008, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01146070

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

M. Boley, S. Moens, and T. Gärtner, Linear space direct pattern sampling using coupling from the past, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.69-77, 2012.

M. Boley, T. Horváth, A. Poigné, and S. Wrobel, Listing closed sets of strongly accessible set systems with applications to data mining, Theoretical Computer Science 411, vol.3, p.40, 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, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery, Data Min. Knowl. Discov. 31, vol.5, pp.1391-1418, 2017.

R. Bonaque, T. D. Cao, B. Cautis, F. Goasdoué, J. Letelier et al., Mixed-instance querying: a lightweight integration architecture for data journalism, PVLDB 9, vol.13, pp.1513-1516, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01321201

F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi, ExAMiner: Optimized level-wise frequent pattern mining with monotone constraints, Third IEEE International Conference on Data Mining, pp.11-18, 2003.

F. Bonchi, F. Giannotti, C. Lucchese, S. Orlando, R. Perego et al., A constraint-based querying system for exploratory pattern discovery, Information Systems, vol.34, pp.3-27, 2009.

G. Bosc, J. Golebiowski, M. Bensafi, C. Robardet, M. Plantevit et al., Local subgroup discovery for eliciting and understanding new structure-odor relationships, International Conference on Discovery Science, 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 Mining and Knowledge Discovery, vol.32, pp.604-650, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01418663

J. Boulicaut and B. Jeudy, Constraint-based data mining, Data mining and knowledge discovery handbook, pp.339-354, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00567915

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

N. Cao, Y. Lin, L. Li, and H. Tong, g-Miner: Interactive Visual Group Mining on Multivariate Graphs, pp.279-288, 2015.

C. J. Carmona, M. J. Del, J. , and F. Herrera, A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy, In: Knowl.-Based Syst, vol.139, pp.89-100, 2018.

C. J. Carmona, P. González, M. Jesús, and F. Herrera, Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms, Interdiscip. Rev. Data Min. Knowl. Discov. 4, vol.2, p.34, 2014.

D. Caswell and K. Dörr, Automated Journalism 2.0: Event-driven narratives: From simple descriptions to real stories, Journalism practice 12, vol.4, pp.477-496, 2018.

S. Cazalens, P. Lamarre, J. Leblay, I. Manolescu, and X. Tannier, Journalism, Misinformation and Fact Checking" alternate paper track of, The Web Conference, 2018.

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.

D. Cherepnalkoski, A. Karpf, I. Mozeti?, and M. Gr?ar, Cohesion and coalition formation in the European Parliament: roll-call votes and Twitter activities, PloS one 11, vol.11, p.166586, 2016.

G. Ciampaglia, P. Luca, L. M. Shiralkar, J. Rocha, F. Bollen et al., Computational fact checking from knowledge networks, PloS one 10, vol.6, p.128193, 2015.

J. Clinton, S. Jackman, and D. Rivers, The statistical analysis of roll call data, American Political Science Review, vol.98, pp.355-370, 2004.

M. Coddington, Clarifying journalism's quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting, Digital journalism 3.3, pp.331-348, 2015.

J. Cohen, A Coefficient of Agreement for Nominal Scales, Education and Psychological Measurement, vol.20, pp.37-46, 1960.

S. Cohen, C. Li, J. Yang, and C. Yu, Computational Journalism: A Call to Arms to Database Researchers, CIDR. www.cidrdb.org, pp.148-151, 2011.

T. Cover and J. Thomas, Elements of information theory, 2012.

T. F. Cox, A. A. Michael, and . Cox, Multidimensional scaling. Chapman and hall/CRC, vol.151, p.9, 2000.

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, pp.1063-1074, 2011.

B. A. Davey, A. Hilary, and . Priestley, Introduction to lattices and order, 2002.

D. Nooy, A. Wouter, V. Mrvar, and . Batagelj, Exploratory social network analysis with Pajek: Revised and expanded edition for updated software, vol.46, 2018.

J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar et al., Orange: data mining toolbox in python, Journal of Machine Learning Research, vol.14, pp.2349-2353, 2013.

K. Dimitriadou, O. Papaemmanouil, and Y. Diao, Explore-by-example: an automatic query steering framework for interactive data exploration, Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp.517-528, 2014.

L. Diop, C. Talibouya-diop, A. Giacometti, D. Li, and A. Soulet, Sequential Pattern Sampling with Norm Constraints, ICDM. IEEE Computer Society, vol.37, p.34, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01889230

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, ICDM. IEEE Computer Society, pp.111-120, 2015.

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.

X. Du, W. Duivesteijn, and M. Pechenizkiy, ELBA: Exceptional Learning Behavior Analysis, EDM. International Educational Data Mining Society (IEDMS), 2018.

W. Duivesteijn, A. Feelders, and A. J. Knobbe, Different slopes for different folks: mining for exceptional regression models with cook's distance, pp.868-876, 2012.

. Duivesteijn, . Wouter, J. Ad, A. Feelders, and . Knobbe, Exceptional model mining, Data Mining and Knowledge Discovery, vol.30, pp.47-98, 2016.

. Duivesteijn, A. J. Wouter, and . Knobbe, Exploiting False Discoveries -Statistical Validation of Patterns and Quality Measures in Subgroup Discovery, ICDM. IEEE Computer Society, vol.101, pp.151-160, 2011.

. Duivesteijn, A. J. Wouter, A. Knobbe, M. Feelders, and . Van-leeuwen, Subgroup Discovery Meets Bayesian Networks -An Exceptional Model Mining Approach, ICDM. IEEE Computer Society, vol.42, p.43, 2010.

O. Dunn and . Jean, Multiple comparisons among means, Journal of the American statistical association, vol.56, pp.52-64, 1961.

F. Duris, J. Gazdarica, I. Gazdaricova, L. Strieskova, and J. Budis, Mean and variance of ratios of proportions from categories of a multinomial distribution, Turna, and Tomas Szemes, vol.5, p.109, 2018.

V. Dzyuba, Mine, Interact, Learn, Repeat: Interactive Pattern-based Data Exploration ; Zoek, Interacteer, Leer, Herhaal: interactieve data-exploratie met patronen, vol.145, 2017.

V. Dzyuba, M. Van-leeuwen, and L. Raedt, Flexible constrained sampling with guarantees for pattern mining, Data Mining and Knowledge Discovery, vol.31, pp.1266-1293, 2017.

V. Dzyuba, M. Van-leeuwen, S. Nijssen, and L. Raedt, Interactive Learning of Pattern Rankings, International Journal on Artificial Intelligence Tools, vol.23, issue.6, p.145, 2014.

B. Efron and R. J. Tibshirani, An introduction to the bootstrap, p.116, 1994.

T. Eiter and H. Mannila, Computing discrete Fréchet distance, p.153, 1994.

R. Ennals, B. Trushkowsky, and J. M. Agosta, Highlighting disputed claims on the web, Proceedings of the 19th international conference on World wide web, pp.341-350, 2010.

D. Eppstein and D. S. Hirschberg, Choosing subsets with maximum weighted average, J. Algorithms, vol.24, issue.1, pp.112-114, 1997.

S. Erevelles, N. Fukawa, and L. Swayne, Big Data consumer analytics and the transformation of marketing, Journal of Business Research, vol.69, pp.897-904, 2016.

V. Etter, J. Herzen, M. Grossglauser, and P. Thiran, Mining democracy, COSN. ACM, pp.1-12, 2014.

U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, From data mining to knowledge discovery in databases, AI magazine 17, vol.3, pp.37-37, 1996.

J. L. Fleiss, Measuring nominal scale agreement among many raters, Psychological Bulletin, vol.76, pp.378-382, 1971.

T. Flew, C. Spurgeon, A. Daniel, and A. Swift, The promise of computational journalism, Journalism Practice 6, vol.2, pp.157-171, 2012.

S. Fortunato, Community detection in graphs, Physics reports, vol.486, issue.3-5, pp.75-174, 2010.

. Freeman and C. Linton, A set of measures of centrality based on betweenness, Sociometry, pp.35-41, 1977.

P. M. Fukunage and . Narendra, A branch and bound algorithm for computing k-nearest neighbors, IEEE transactions on computers 7, vol.152, p.151, 1975.

J. Fürnkranz and P. A. Flach, Roc 'n'rule learning-towards a better understanding of covering algorithms, Machine Learning 58.1, vol.33, p.45, 2005.

J. Fürnkranz, D. Gamberger, and N. Lavra?, Foundations of rule learning, vol.157, p.86, 2012.

E. Galbrun and P. Miettinen, Redescription Mining, Springer Briefs in Computer Science, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01726072

, Mining Redescriptions with Siren, 2018.

B. Ganter and S. O. Kuznetsov, Pattern Structures and Their Projections, Lecture Notes in Computer Science, vol.2120, p.59, 2001.

B. Ganter and R. Wille, Formal concept analysis -mathematical foundations, vol.27, p.47, 1999.

B. Ganter, S. Obiedkov, S. Rudolph, and G. Stumme, Conceptual exploration, vol.30, p.27, 2016.

K. Garimella, G. De-francisci, and . Morales, Quantifying Controversy on Social Media". In: ACM Trans. Social Computing 1, vol.1, pp.1-3, 2018.

G. C. Garriga, P. Kralj, and N. Lavra?, Closed sets for labeled data, Journal of Machine Learning Research 9, vol.159, p.36, 2008.

A. Gatt and E. Reiter, SimpleNLG: A Realisation Engine for Practical Applications, The 12th European Workshop on Natural Language Generation. ENLG, vol.135, 2009.

S. Geisser, Predictive Inference, vol.55, 1993.

L. Geng and H. J. Hamilton, Interestingness measures for data mining: A survey, ACM Comput. Surv, vol.38, issue.3, p.31, 2006.

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

, Dense Neighborhood Pattern Sampling in Numerical Data, SDM. SIAM, vol.37, p.34, 2018.

L. A. Goodman, The Multivariate Analysis of Qualitative Data: Interaction Among Multiple Classifications, Journal of the American Statistical Association, vol.65, pp.226-256, 1970.

H. Grosskreutz, Cascaded subgroups discovery with an application to regression, Proc. ECML/PKDD, vol.5211, p.33, 2008.

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

H. Grosskreutz, B. Lang, and D. Trabold, A Relevance Criterion for Sequential Patterns, ECML/PKDD (1), vol.8188, pp.369-384, 2013.

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

H. Grosskreutz, S. Rüping, and S. Wrobel, Tight Optimistic Estimates for Fast Subgroup Discovery, ECML/PKDD, vol.5211, p.49, 2008.

W. Hämäläinen, Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance, ICDM. IEEE Computer Society, pp.196-205, 2010.

, StatApriori: an efficient algorithm for searching statistically significant association rules, Knowl. Inf. Syst, vol.23, issue.3, pp.373-399, 2010.

, Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures, Knowl. Inf. Syst, vol.32, pp.383-414, 2012.

W. Hämäläinen and G. I. Webb, A tutorial on statistically sound pattern discovery, Data Min. Knowl. Discov, vol.33, pp.325-377, 2019.

J. T. Hamilton and F. Turner, Accountability through algorithm: Developing the field of computational journalism, Report from the Center for Advanced Study in the Behavioral Sciences, Summer Workshop, pp.27-41, 2009.

M. Hammal, H. Ali, L. Mathian, M. Merchez, C. Plantevit et al., Rank correlated subgroup discovery, Journal of Intelligent Information Systems, pp.1-24, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02093496

J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, ACM sigmod record, vol.29, issue.2, p.35, 2000.

F. Harper, J. A. Maxwell, and . Konstan, The MovieLens Datasets: History and Context, ACM Transactions on Interactive Intelligent Systems (TiiS) 5.4, vol.19, p.78, 2016.

N. Hassan, C. Li, and M. Tremayne, Detecting check-worthy factual claims in presidential debates, pp.1835-1838, 2015.

N. Hassan, G. Zhang, F. Arslan, J. Caraballo, D. Jimenez et al., ClaimBuster: The First-ever End-to-end Fact-checking System, Proceedings of the VLDB Endowment, vol.10, 2017.

N. Hassan, F. Arslan, C. Li, and M. Tremayne, Toward automated fact-checking: Detecting check-worthy factual claims by ClaimBuster, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1803-1812, 2017.

A. F. Hayes and K. Krippendorff, Answering the call for a standard reliability measure for coding data, Communication methods and measures 1.1, pp.77-89, 2007.

C. Hébert and B. Crémilleux, A Unified View of Objective Interestingness Measures, Lecture Notes in Computer Science, vol.4571, pp.533-547, 2007.

J. Heer and D. Boyd, Vizster: Visualizing Online Social Networks, INFOVIS. IEEE Computer Society, pp.32-39, 2005.

I. Herman, G. Melançon, and M. Marshall, Graph visualization and navigation in information visualization: A survey, IEEE Transactions on visualization and computer graphics 6.1, pp.24-43, 2000.

F. Herrera, C. Carmona, P. González, and M. Jesus, An overview on subgroup discovery: foundations and applications, Knowledge and information systems, vol.29, pp.495-525, 2011.

S. Hix, Legislative behaviour and party competition in the European Parliament: An application of nominate to the EU, JCMS: Journal of Common Market Studies, vol.39, pp.663-688, 2001.

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, p.97, 1979.

S. Hix, A. Noury, and G. Roland, Dimensions of politics in the European Parliament, vol.50, p.145, 2006.

S. Hix, A. G. Noury, and G. Roland, Democratic politics in the European Parliament, 2007.

S. Holm, A simple sequentially rejective multiple test procedure, Scandinavian journal of statistics, pp.65-70, 1979.

Q. Hu and T. Imielinski, Alpine: Progressive itemset mining with definite guarantees, Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, p.37, 2017.

E. Huang, L. Peng, L. D. Palma, A. Abdelkafi, A. Liu et al., Optimization for Active Learning-based Interactive Database Exploration, PVLDB 12.1, pp.71-84, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01969886

C. Ireton and J. Posetti, Journalism, fake news & disinformation: handbook for journalism education and training, 2018.

A. Jakulin, W. Buntine, T. Pira, and H. Brasher, Analyzing the us senate in 2003: Similarities, clusters, and blocs, Political Analysis, vol.17, p.82, 2009.

F. Janssen and J. Fürnkranz, On Trading Off Consistency and Coverage in Inductive Rule Learning, vol.1, pp.306-313, 2006.

M. Jesús, P. Del, F. González, M. Herrera, and . Mesonero, Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing, IEEE Trans. Fuzzy Systems, vol.15, pp.578-592, 2007.

B. Jeudy and J. Boulicaut, Optimization of association rule mining queries, Intelligent Data Analysis, vol.6, pp.341-357, 2002.

D. S. Johnson, C. H. Papadimitriou, and M. Yannakakis, On Generating All Maximal Independent Sets, Inf. Process. Lett, vol.27, issue.3, pp.119-123, 1988.

D. Johnson and S. Sinanovic, Symmetrizing the kullback-leibler distance, IEEE Transactions on Information Theory, vol.62, 2001.

G. Karypis and V. Kumar, METIS -Unstructured Graph Partitioning and Sparse Matrix Ordering System, 1995.

B. Kav?ek and N. Lavra?, APRIORI-SD: Adapting association rule learning to subgroup discovery, Applied Artificial Intelligence, vol.20, p.34, 2006.

. Kaytoue, S. O. Mehdi, A. Kuznetsov, and . Napoli, Revisiting Numerical Pattern Mining with Formal Concept Analysis, IJCAI. IJCAI/AAAI, vol.29, pp.1342-1347, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00600222

M. Kaytoue, S. O. Kuznetsov, A. Napoli, and S. Duplessis, Mining gene expression data with pattern structures in formal concept analysis, Information Sciences 181.10, pp.1989-2001, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00541100

M. Kaytoue, M. Plantevit, and A. Zimmermann, Exceptional contextual subgraph mining, Anes Bendimerad, and Céline Robardet, vol.18, pp.1-41, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01488732

G. M. Kempen, L. J. Kempenvan, and . Van-vliet, Mean and variance of ratio estimators used in fluorescence ratio imaging, Cytometry: The Journal of the International Society for Analytical Cytology, vol.39, pp.300-305, 2000.

M. G. Kendall, A New Measure of Rank Correlation, Biometrika 30.1, pp.81-93, 1938.

M. Kendall and . George, Rank correlation methods, 1948.

M. G. Kendall, A. Stuart, and J. K. Ord, Kendall's advanced theory of statistics. v. 1: Distribution theory, vol.110, 1994.

M. Kirchgessner, V. Leroy, S. Amer-yahia, and S. Mishra, Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns, pp.547-556, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01407787

A. Kleiner, A. Talwalkar, P. Sarkar, and M. I. Jordan, The Big Data Bootstrap". In: ICML. icml.cc / Omnipress, p.117, 2012.

W. Kloesgen, Subgroup Mining, Computational Intelligence in Data Mining, pp.39-49, 2000.

W. Klösgen, Explora: A Multipattern and Multistrategy Discovery Assistant, Advances in Knowledge Discovery and Data Mining, pp.249-271, 1996.

W. Klösgen, Data mining tasks and methods: subgroup discovery: deviation analysis, Handbook of data mining and knowledge discovery, pp.354-361, 2002.

W. Klösgen and M. May, Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database, Lecture Notes in Computer Science, vol.2431, pp.275-286, 2002.

R. Kohavi, The Power of Decision Tables, Lecture Notes in Computer Science, vol.912, pp.174-189, 1995.

, Mining e-commerce data: the good, the bad, and the ugly, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp.8-13, 2001.

J. Komiyama, M. Ishihata, H. Arimura, T. Nishibayashi, and S. Minato, Statistical emerging pattern mining with multiple testing correction, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.897-906, 2017.

B. Kovach and T. Rosenstiel, The Elements of Journalism, 2014.

T. E. Krak and A. Feelders, Exceptional Model Mining with Tree-Constrained Gradient Ascent, SDM. SIAM, pp.487-495, 2015.

P. Kralj-novak, N. Lavrac, and G. I. Webb, Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining, Journal of Machine Learning Research, vol.10, pp.377-403, 2009.

K. Novak, N. Petra, G. I. Lavra?, and . Webb, Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining, J. Mach. Learn. Res, vol.10, 2009.

K. Krippendorff, Context Analysis: An Introduction to Its Methodology, Sage, vol.105, p.104, 1980.

, Content Analysis, An introduction to its methodology, vol.100, 2004.

K. Krippendorff, Y. Mathet, S. Bouvry, and A. Widlöcher, On the Reliability of Unitizing Textual Continua: Further Developments, Qual. Quant, vol.50, pp.2347-2364, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01712554

A. Kucharski, Post-truth: Study epidemiology of fake news, Nature 540, vol.7634, p.525, 2016.

S. O. Kuznetsov, A Fast Algorithm for Computing All Intersections of Objects in a Finite Semi-lattice, Nauchno-Tekhnicheskaya Informatsiya ser. 2.1, vol.46, pp.17-20, 1993.

, Learning of Simple Conceptual Graphs from Positive and Negative Examples, Lecture Notes in Computer Science, vol.1704, pp.384-391, 1999.

S. O. Kuznetsov and S. A. Obiedkov, Comparing performance of algorithms for generating concept lattices, Journal of Experimental & Theoretical Artificial Intelligence, vol.14, issue.2-3, p.40, 2002.

C. Lacombe, A. De, A. Morel, F. Belfodil, C. Portet et al., Analyse de comportements relatifs exceptionnels expliquée par des textes : les votes du parlement européen, Extraction et Gestion des connaissances, EGC 2019, pp.437-440, 2019.

A. H. Land and A. G. Doig, An Automatic Method of Solving Discrete Programming Problems, Econometrica 28.3, pp.497-520, 1960.

N. Lavra?, P. Flach, and B. Zupan, Rule evaluation measures: A unifying view, International Conference on Inductive Logic Programming, pp.174-185, 1999.

N. Lavrac, P. A. Flach, and B. Zupan, Rule Evaluation Measures: A Unifying View, Inductive Logic Programming, 9th International Workshop, ILP-99, vol.1634, pp.174-185, 1999.

N. Lavra?, B. Kav?ek, P. Flach, and L. Todorovski, Subgroup discovery with CN2-SD, Journal of Machine Learning Research 5.Feb, pp.153-188, 2004.

N. Lavrac, B. Kavsek, P. A. Flach, and L. Todorovski, Subgroup Discovery with CN2-SD, Journal of Machine Learning Research, vol.5, pp.153-188, 2004.

L. Cam, L. , G. Lo, and Y. , Asymptotics in statistics: some basic concepts, 2012.

M. Leeuwen and . Van, Interactive Data Exploration Using Pattern Mining, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, vol.8401, pp.169-182, 2014.

M. Leeuwen, A. J. Van-and, and . Knobbe, Non-redundant Subgroup Discovery in Large and Complex Data, ECML/PKDD, vol.6913, pp.459-474, 2011.

, Diverse subgroup set discovery, Data Min. Knowl. Discov. 25, vol.2, p.45, 2012.

J. Lehmann, D. Gerber, M. Morsey, and A. Ngomo, Defacto-deep fact validation, International semantic web conference, pp.312-327, 2012.

D. Leman, A. Feelders, and A. Knobbe, Exceptional model mining, vol.18, p.44, 2008.

F. Lemmerich, M. Atzmueller, and F. Puppe, Fast exhaustive subgroup discovery with numerical target concepts, Data Min. Knowl. Discov, vol.30, issue.3, pp.33-35, 2016.

F. Lemmerich and M. Becker, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2018, vol.11053, pp.658-662, 2018.

F. Lemmerich, M. Becker, and M. Atzmueller, Generic Pattern Trees for Exhaustive Exceptional Model Mining, ECML/PKDD, vol.7524, p.40, 2012.

F. Lemmerich, M. Ifl, and F. Puppe, Identifying influence factors on students success by subgroup discovery, Educational Data Mining, 2011.

F. Lemmerich, M. Rohlfs, and M. Atzmueller, Fast Discovery of Relevant Subgroup Patterns, FLAIRS Conference, vol.159, p.34, 2010.

F. Lemmerich, M. Becker, P. Singer, D. Helic, A. Hotho et al., Mining Subgroups with Exceptional Transition Behavior, KDD. ACM, pp.965-974, 2016.

P. Lenca, P. Meyer, B. Vaillant, and S. Lallich, On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, European Journal of Operational Research, vol.184, pp.610-626, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02316548

G. Li and M. J. Zaki, Sampling frequent and minimal boolean patterns: theory and application in classification, Data Mining and Knowledge Discovery, vol.30, pp.181-225, 2016.

J. Li, A. Wai-chee, H. Fu, J. He, H. Chen et al., Mining risk patterns in medical data, pp.770-775, 2005.

J. Lijffijt, B. Kang, W. Duivesteijn, K. Puolamäki, E. Oikarinen et al., Subjectively Interesting Subgroup Discovery on Real-Valued Targets, ICDE. IEEE Computer Society, pp.1352-1355, 2018.

J. D. Little, G. Katta, . Murty, W. Dura, C. Sweeney et al., An algorithm for the traveling salesman problem, Operations research 11, pp.972-989, 1963.

B. Liu, W. Hsu, and Y. Ma, Integrating Classification and Association Rule Mining, KDD, pp.80-86, 1998.

F. Llinares-lópez, M. Sugiyama, L. Papaxanthos, and K. Borgwardt, Fast and memory-efficient significant pattern mining via permutation testing, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.725-734, 2015.

B. T. Lowerre, The HARPY speech recognition system, p.36, 1976.

T. Lucas, R. Vimieiro, and T. B. Ludermir, SSDP+: A Diverse and More Informative Subgroup Discovery Approach for High Dimensional Data, vol.36, p.34, 2018.

L. Lumpe and S. E. Schmidt, Pattern Structures and Their Morphisms, CEUR Workshop Proceedings. CEUR-WS.org, vol.1466, pp.171-179, 2015.

J. Luna, J. María, C. Romero, S. Romero, and . Ventura, Discovering Subgroups by Means of Genetic Programming, Lecture Notes in Computer Science, vol.7831, pp.121-132, 2013.

M. Mampaey, S. Nijssen, A. Feelders, and A. J. Knobbe, Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data, ICDM. IEEE Computer Society, vol.34, pp.499-508, 2012.

I. Manolescu, ContentCheck: Content Management Techniques and Tools for Fact-checking, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01596563

R. Mathonat, D. Nurbakova, J. F. Boulicaut, and M. Kaytoue, SeqScout: using a bandit algorithm to discover interesting subgroups in Labeled Sequences, p.21, 2019.

M. Meeng, J. Arno, and . Knobbe, Flexible Enrichment with Cortana -Software Demo, Proceedings Benelearn, vol.156, p.35, 2011.

M. Meil?, Comparing clusterings-an information based distance, Journal of multivariate analysis 98, vol.5, pp.873-895, 2007.

. Minato, T. Shin-ichi, K. Uno, A. Tsuda, J. Terada et al., A Fast Method of Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration, ECML/PKDD, vol.8725, p.52, 2014.

S. Moens and M. Boley, Instant exceptional model mining using weighted controlled pattern sampling, International Symposium on Intelligent Data Analysis, vol.72, p.40, 2014.

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

M. Moranges, M. Plantevit, A. P. Fournel, M. Bensafi, and C. Robardet, Exceptional Attributed Subgraph Mining to Understand the Olfactory Percept, Discovery Science -21st International Conference, DS 2018, vol.11198, pp.276-291, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01878375

S. Morishita and J. Sese, Traversing Itemset Lattice with Statistical Metric Pruning, PODS. ACM, pp.226-236, 2000.

N. Mukhopadhyay, Probability and statistical inference, vol.115, 2000.

I. M. Mullins, S. Mir, J. Siadaty, K. Lyman, . Scully et al., Data mining and clinical data repositories: Insights from a 667,000 patient data set, Computers in biology and medicine 36, vol.12, pp.1351-1377, 2006.

F. Murtagh and P. Contreras, Algorithms for hierarchical clustering: an overview, Interdiscip. Rev. Data Min. Knowl. Discov, vol.2, issue.1, pp.86-97, 2012.

P. M. Narendra and K. Fukunaga, A branch and bound algorithm for feature subset selection, IEEE Transactions on computers 9, pp.917-922, 1977.

J. Neter, H. Michael, . Kutner, J. Christopher, W. Nachtsheim et al., Applied linear statistical models, 1996.

M. Newman and . Ej, Detecting community structure in networks, The European Physical Journal B 38, vol.2, pp.321-330, 2004.

A. T. Nguyen, A. Kharosekar, M. Lease, and B. C. Wallace, An Interpretable Joint Graphical Model for Fact-Checking From Crowds, pp.1511-1518, 2018.

S. Nijssen and A. Zimmermann, Constraint-based pattern mining, Frequent pattern mining, pp.147-163, 2014.

J. R. Norris, Markov chains. 2, vol.43, p.11, 1998.

. Omidvar-tehrani, S. Behrooz, and . Amer-yahia, Online Lattice-Based Abstraction of User Groups, DEXA (1), vol.10438, pp.95-110, 2017.

, User Group Analytics: Discovery, Exploration and Visualization, CIKM. ACM, pp.2307-2308, 2018.

. Omidvar-tehrani, S. Behrooz, and . Amer-yahia, User Group Analytics Survey and Research Opportunities, IEEE Transactions on Knowledge and Data Engineering, 2019.

. Omidvar-tehrani, S. Behrooz, R. M. Amer-yahia, and . Borromeo, User group analytics: hypothesis generation and exploratory analysis of user data, J. 28, vol.2, pp.243-266, 2019.

. Omidvar-tehrani, S. Behrooz, . Amer-yahia, V. S. Laks, and . Lakshmanan, Cohort Representation and Exploration, pp.169-178, 2018.

. Omidvar-tehrani, S. Behrooz, A. Amer-yahia, and . Termier, Interactive User Group Analysis, CIKM. ACM, vol.10, pp.403-412, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01403238

. Omidvar-tehrani, S. Behrooz, P. Amer-yahia, D. Dutot, and . Trystram, Multi-Objective Group Discovery on the Social Web, ECML/PKDD, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01297763

, Lecture Notes in Computer Science, vol.9851, pp.296-312

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?, In: BMC health services research, vol.12, p.365, 2012.

A. Pajala, A. Jakulin, and W. Buntine, Parliamentary group and individual voting behaviour in the Finnish parliament in year 2003: a group cohesion and voting similarity analysis, 2004.

L. Palen and A. L. Hughes, Social media in disaster communication, Handbook of disaster research, pp.497-518, 2018.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering Frequent Closed Itemsets for Association Rules, Lecture Notes in Computer Science, vol.1540, p.27, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00467747

K. Pearson, A. Lee, E. Warren, A. Fry, and C. D. Fawcett, Mathematical Contributions to the Theory of Evolution: IX. On the Principle of Homotyposis and its Relation to Heredity, to Variability of the Individual, and to that of Race. Part I: Homotyposis in the Vegetable Kingdom, Philosophical Transactions of the Royal Society 197.Series A, pp.285-379, 1901.

L. Pellegrina and F. Vandin, Efficient Mining of the Most Significant Patterns with Permutation Testing, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2070-2079, 2018.

G. Piatetsky-shapiro, Discovery, Analysis, and Presentation of Strong Rules, Knowledge Discovery in Databases, pp.229-248, 1991.

B. F. Pieters, A. Knobbe, and S. Dzeroski, Subgroup discovery in ranked data, with an application to gene set enrichment, Proceedings preference learning workshop (PL 2010) at ECML PKDD, vol.10, p.33, 2010.

S. Pool, F. Bonchi, and M. Van-leeuwen, Description-Driven Community Detection, ACM TIST 5, vol.2, p.28, 2014.

K. T. Poole and H. Rosenthal, A spatial model for legislative roll call analysis, American Journal of Political Science, vol.151, p.145, 1985.

, Congress: A political-economic history of roll call voting, 2000.

F. Portet, E. Reiter, A. Gatt, J. Hunter, S. Sripada et al., Automatic generation of textual summaries from neonatal intensive care data, Artificial Intelligence 173.7-8, pp.789-816, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00953707

C. Rajski, A metric space of discrete probability distributions, pp.371-377, 1961.

M. Riondato and F. Vandin, MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, vol.45, p.37, 2018.

E. Roddy and M. Doherty, Gout. Epidemiology of gout, Arthritis research & therapy 12, vol.6, p.86, 2010.

S. Roman, Lattices and ordered sets, vol.27, 2008.

C. Romero and S. Ventura, Data mining in education, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3.1, pp.12-27, 2013.

C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Handbook of educational data mining, 2010.

G. Rossetti and R. Cazabet, Community Discovery in Dynamic Networks: A Survey, ACM Comput. Surv. 51, vol.2, p.37, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01658399

C. Sá, W. Rebelo-de, C. Duivesteijn, A. J. Soares, and . Knobbe, Exceptional Preferences Mining, vol.43, p.11, 2016.

C. Sá, W. Rebelo-de, P. Duivesteijn, A. Azevedo, C. Mário-jorge et al., Discovering a taste for the unusual: exceptional models for preference mining, Machine Learning 107, vol.11, p.11, 2018.

M. A. Schwartz, The importance of stupidity in scientific research, Journal of Cell Science, vol.121, pp.1771-1771, 2008.

W. A. Scott, Reliability of Content Analysis: The Case of Nominal Scale Coding, Public Opinion Quarterly, vol.19, pp.321-325, 1955.

L. G. Shapiro, M. Robert, and . Haralick, A metric for comparing relational descriptions, IEEE Transactions on Pattern Analysis & Machine Intelligence 1, pp.90-94, 1985.

S. Shapiro, . Sanford, B. Martin, and . Wilk, An analysis of variance test for normality (complete samples), Biometrika 52.3/4, pp.591-611, 1965.

A. Siebes, Data Surveying: Foundations of an Inductive Query Language, KDD. AAAI Press, pp.269-274, 1995.

A. Siebes, J. Vreeken, and M. Van-leeuwen, Item Sets that Compress, SDM. SIAM, pp.395-406, 2006.

R. Silva, . Nunes-moni, A. Da, C. Spritzer, and . Freitas, Visualization of Roll Call Data for Supporting Analyses of Political Profiles, 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, pp.150-157, 2018.

F. Simini, M. C. González, A. Maritan, and A. Barabási, A universal model for mobility and migration patterns, p.96, 2012.

P. Singer, D. Helic, A. Hotho, and M. Strohmaier, HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web, pp.1003-1013, 2015.

J. A. Smith and M. Osborn, Interpretative phenomenological analysis, Doing social psychology research, pp.229-254, 2004.

M. Sozio and A. Gionis, The community-search problem and how to plan a successful cocktail party, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.939-948, 2010.

C. Spearman, The Proof and Measurement of Association Between Two Things, American Journal of Psychology, vol.15, pp.72-101, 1904.

M. Spiegelman, C. Terwilliger, and F. Fearing, The reliability of agreement in content analysis, Journal of Social Psychology, vol.37, pp.175-187, 1953.

E. Spyropoulou, T. De-bie, and M. Boley, Interesting pattern mining in multi-relational data, Data Mining and Knowledge Discovery, vol.28, pp.34-36, 2014.

J. Steele and . Michael, The Cauchy-Schwarz master class: an introduction to the art of mathematical inequalities, vol.115, 2004.

. Tan, V. Pang-ning, J. Kumar, and . Srivastava, Selecting the right objective measure for association analysis, Inf. Syst, vol.29, pp.31-33, 2004.

R. E. Tarone, A modified Bonferroni method for discrete data, Biometrics, pp.515-522, 1990.

A. Terada, D. Duverle, and K. Tsuda, Significant Pattern Mining with Confounding Variables, Lecture Notes in Computer Science, vol.9651, issue.1, pp.277-289, 2016.

A. Terada, K. Tsuda, and J. Sese, Fast Westfall-Young permutation procedure for combinatorial regulation discovery, 2013 IEEE International Conference on Bioinformatics and Biomedicine, pp.153-158, 2013.

A. Terada, M. Okada-hatakeyama, K. Tsuda, and J. Sese, Statistical significance of combinatorial regulations, Proceedings of the National Academy of Sciences 110.32, vol.164, pp.12996-13001, 2013.

L. Todorovski, P. Flach, and N. Lavra?, Predictive performance of weighted relative accuracy, European Conference on Principles of Data Mining and Knowledge Discovery, pp.255-264, 2000.

I. Trajkovski, N. Lavra?, and J. Tolar, SEGS: Search for enriched gene sets in microarray data, Journal of biomedical informatics 41, vol.4, pp.588-601, 2008.

K. Trohidis, G. Tsoumakas, G. Kalliris, and I. P. Vlahavas, Multi-Label Classification of Music into Emotions, pp.325-330, 2008.

J. W. Tukey, Exploratory data analysis, vol.18, p.7, 1977.

. Van-leeuwen and . Matthijs, Interactive data exploration using pattern mining, Interactive knowledge discovery and data mining in biomedical informatics, pp.169-182, 2014.

S. Ventura and J. Luna, Supervised Descriptive Pattern Mining, p.41, 2018.

J. Vizzini, C. Labbé, and F. Portet, Génération automatique de billets journalistiques : singularité et normalité d'une sélection, Extraction et Gestion des Connaissances, p.2017, 2017.

A. Computationnel, , vol.135

A. Vlachos and S. Riedel, Fact Checking: Task definition and dataset construction, Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp.18-22, 2014.

E. Voeten, Enlargement and the'normal'European parliament, The legitimacy of the European union after enlargement, pp.93-113, 2009.

J. Vreeken, M. Van-leeuwen, and A. Siebes, Krimp: mining itemsets that compress, Data Min. Knowl. Discov. 23, vol.1, p.23, 2011.

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

S. Wasserman and K. Faust, Social network analysis: Methods and applications, vol.8, 1994.

G. I. Webb, Discovering associations with numeric variables, KDD. ACM, pp.383-388, 2001.

, Discovering significant rules, pp.434-443, 2006.

G. I. Webb, Discovering significant patterns, Machine learning 68.1, pp.1-33, 2007.

G. I. Webb, Layered critical values: a powerful direct-adjustment approach to discovering significant patterns, Machine Learning, vol.71, pp.307-323, 2008.

P. H. Westfall and . Stanley-young, Resampling-based multiple testing: Examples and methods for p-value adjustment, vol.279, 1993.

D. Whitley, A genetic algorithm tutorial, Statistics and computing 4, vol.2, p.36, 1994.

R. Wille, Restructuring lattice theory: an approach based on hierarchies of concept, Ordered sets, 1982.

S. Wrobel, An Algorithm for Multi-relational Discovery of Subgroups, Lecture Notes in Computer Science. Springer, vol.1263, p.51, 1997.

, Inductive logic programming for knowledge discovery in databases, Relational data mining, pp.74-101, 2001.

. Wu, Computational Journalism: from Answering Questions to Questioning Answers and Raising Good Questions, 2015.

Y. Wu, K. Pankaj, C. Agarwal, J. Li, C. Yang et al., Toward computational fact-checking, Proceedings of the VLDB Endowment, pp.589-600, 2014.

Y. Wu, J. Gao, K. Pankaj, J. Agarwal, and . Yang, Finding diverse, highvalue representatives on a surface of answers, Proceedings of the VLDB Endowment 10, vol.7, p.130, 2017.

R. Xu, D. C. Wunsch, and I. I. , Survey of clustering algorithms, IEEE Trans. Neural Networks, vol.16, pp.645-678, 2005.

X. Yan and J. Han, gSpan: Graph-Based Substructure Pattern Mining, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002, pp.721-724, 2002.

. Yang, P. K. Jun, S. Agarwal, B. Roy, Y. Walenz et al., Query Perturbation Analysis: An Adventure of Database Researchers in Fact-Checking, IEEE Data Eng. Bull, vol.41, issue.3, pp.28-42, 2018.

M. Young, A. Lynn, and . Hermida, From Mr. and Mrs. outlier to central tendencies: Computational journalism and crime reporting at the Los Angeles Times, Digital Journalism 3.3, pp.381-397, 2015.

M. Zaki and . Javeed, Scalable algorithms for association mining, IEEE transactions on knowledge and data engineering, vol.12, pp.372-390, 2000.

S. Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine 17.3, vol.153, p.37, 1996.

A. Zimmermann and L. De-raedt, Cluster-grouping: from subgroup discovery to clustering, Machine Learning 77.1, vol.49, p.34, 2009.

G. Zipf and . Kingsley, The p 1 p 2/d hypothesis: The case of railway express, The Journal of Psychology, vol.22, pp.3-8, 1946.

M. Zitnik, F. Nguyen, B. Wang, J. Leskovec, A. Goldenberg et al., Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities, Information Fusion, vol.50, pp.71-91, 2019.

, MOTS-CLÉS : découverte de sous-groupes intéressants, fouille de modèles exceptionnels

, Laboratoire (s) de recherche : Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) Directeur de thèse

, Président de jury : -Composition du jury : Sihem Amer-Yahia (Directrice de recherche, CNRS) Arno Siebes

A. Knobbe,

S. Cazalens,

M. Plantevit,