M. Atzmüeller, S. Doerfel, and F. Mitzlaff, Description-oriented community detection using exhaustive subgroup discovery, Information Sciences, vol.329, pp.965-984, 2016.
DOI : 10.1016/j.ins.2015.05.008

A. A. Bendimerad, M. Plantevit, and C. Robardet, Unsupervised Exceptional Attributed Sub-Graph Mining in Urban Data, 2016 IEEE 16th International Conference on Data Mining (ICDM), pp.21-30, 2016.
DOI : 10.1109/ICDM.2016.0013
URL : https://hal.archives-ouvertes.fr/hal-01430622

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, KDD '11, pp.582-590, 2011.
DOI : 10.1145/2020408.2020500

J. Boulicaut, M. Plantevit, and C. Robardet, Local Pattern Detection in Attributed Graphs, pp.168-183, 2016.
DOI : 10.1109/69.846291

W. Duivesteijn, A. Feelders, and A. J. Knobbe, Exceptional Model Mining, Data Mining and Knowledge Discovery, vol.77, issue.1, pp.47-98, 2016.
DOI : 10.1007/s10994-009-5121-y
URL : http://www.cs.uu.nl/groups/ADA/pubs/2008/exceptional_model_mining-leman%2Cfeelders%2Cknobbe.pdf

W. Duivesteijn, A. J. Knobbe, A. Feelders, and M. Van-leeuwen, Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach, 2010 IEEE International Conference on Data Mining, pp.158-167, 2010.
DOI : 10.1109/ICDM.2010.53

V. Dzyuba, M. Van-leeuwen, and L. D. Raedt, Flexible constrained sampling with guarantees for pattern mining, Data Mining and Knowledge Discovery, vol.2, issue.1, 2017.
DOI : 10.1145/1133905.1133916

G. L. Falher, A. Gionis, and M. Mathioudakis, Where is the soho of rome? measures and algorithms for finding similar neighborhoods in cities, pp.228-237, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01134117

A. Giacometti and A. Soulet, Frequent Pattern Outlier Detection Without Exhaustive Mining, PAKDD 2016, pp.196-207, 2016.
DOI : 10.1007/978-3-319-31750-2_16
URL : https://hal.archives-ouvertes.fr/hal-01280595

S. Günnemann, I. Färber, B. Boden, and T. Seidl, Subspace clustering meets dense subgraph mining, pp.845-850, 2010.

M. A. Hasan and M. J. Zaki, Output space sampling for graph patterns, Proceedings of the VLDB Endowment, vol.2, issue.1, pp.730-741, 2009.
DOI : 10.14778/1687627.1687710

M. Kaytoue, M. Plantevit, A. Zimmermann, A. Bendimerad, and C. Robardet, Exceptional contextual subgraph mining, Machine Learning, vol.25, issue.2, pp.1-41, 2017.
DOI : 10.1145/1557019.1557125
URL : https://hal.archives-ouvertes.fr/hal-01488732

S. O. Kuznetsov, Learning of Simple Conceptual Graphs from Positive and Negative Examples, Third European Conference , PKDD '99 Proceedings, pp.384-391, 1999.
DOI : 10.1007/978-3-540-48247-5_47

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.

M. Van-leeuwen, Maximal exceptions with minimal descriptions, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.259-276, 2010.
DOI : 10.1001/jama.1961.03040290005002

M. Van-leeuwen and A. J. Knobbe, Diverse subgroup set discovery, Data Mining and Knowledge Discovery, vol.3, issue.1, pp.208-242, 2012.
DOI : 10.1007/s10618-010-0202-x

D. Leman, A. Feelders, and A. J. Knobbe, Exceptional Model Mining, pp.1-16, 2008.
DOI : 10.1007/978-3-540-87481-2_1

F. Lemmerich, M. Becker, P. Singer, D. Helic, A. Hotho et al., Mining Subgroups with Exceptional Transition Behavior, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pp.965-974, 2016.
DOI : 10.1007/s10994-009-5121-y

G. Li and M. J. Zaki, Sampling minimal frequent boolean (DNF) patterns, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.181-225, 2016.
DOI : 10.1145/2339530.2339547
URL : http://www.cs.rpi.edu/%7Ezaki/PaperDir/SIGKDD12.pdf

S. Moens and M. Boley, Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling, pp.203-214, 2014.
DOI : 10.1007/978-3-319-12571-8_18

S. Moens and B. Goethals, Randomly sampling maximal itemsets, Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, IDEA '13, pp.79-86, 2013.
DOI : 10.1145/2501511.2501523
URL : http://poloclub.gatech.edu/idea2013/papers/p80-moens.pdf

F. Moser, R. Colak, A. Rafiey, and M. Ester, Mining Cohesive Patterns from Graphs with Feature Vectors, pp.593-604, 2009.
DOI : 10.1137/1.9781611972795.51

P. Mougel, C. Rigotti, M. Plantevit, and O. Gandrillon, Finding maximal homogeneous clique sets, Knowledge and Information Systems, vol.2, issue.1, pp.579-608, 2014.
DOI : 10.14778/1687627.1687709
URL : https://hal.archives-ouvertes.fr/hal-00827164

P. K. 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.

S. Park, M. Bourqui, and E. Frías-martínez, MobInsight: Understanding Urban Mobility with Crowd-Powered Neighborhood Characterizations, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp.1312-1315, 2016.
DOI : 10.1109/ICDMW.2016.0192

A. Prado, M. Plantevit, C. Robardet, and J. Boulicaut, Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.9, pp.2090-2104, 2013.
DOI : 10.1109/TKDE.2012.154
URL : https://hal.archives-ouvertes.fr/hal-01351727

P. Rozenshtein, A. Anagnostopoulos, A. Gionis, and N. Tatti, Event detection in activity networks, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '14, pp.1176-1185, 2014.
DOI : 10.1145/2623330.2623674

T. K. Saha and M. A. Hasan, A sampling based method for top-k frequent subgraph mining, Stat. An. & DM, vol.8, issue.4, pp.245-261, 2015.
DOI : 10.1002/sam.11277
URL : http://arxiv.org/pdf/1409.1152

A. Silva, W. M. Jr, and M. J. Zaki, Mining attribute-structure correlated patterns in large attributed graphs, Proceedings of the VLDB Endowment, vol.5, issue.5, pp.466-477, 2012.
DOI : 10.14778/2140436.2140443
URL : http://arxiv.org/abs/1201.6568

S. E. Spielman and J. Thill, Social area analysis, data mining, and GIS, Computers, Environment and Urban Systems, vol.32, issue.2, pp.110-122, 2008.
DOI : 10.1016/j.compenvurbsys.2007.11.004

G. Yang, The complexity of mining maximal frequent itemsets and maximal frequent patterns, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.344-353, 2004.
DOI : 10.1145/1014052.1014091