M. Atzmueller, 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

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

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/s10618-015-0403-4

URL : https://openaccess.leidenuniv.nl/bitstream/handle/1887/21760/dissertation.pdf?sequence=22

W. Duivesteijn and A. J. Knobbe, Ad Feelders, and Matthijs van Leeuwen. Subgroup discovery meets bayesian networks ? an exceptional model mining approach, ICDM 2010, pp.158-167, 2010.

A. Géraud-le-falher, M. Gionis, and . Mathioudakis, Where is the soho of Rome? Measures and algorithms for finding similar neighborhoods in cities, ICWSM 2015, pp.228-237, 2015.

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, ICDM 2010, 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

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.

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

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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.2167

S. Moens and M. Boley, Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling, IDA, 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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.353.2537

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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.2030

P. Kralj-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.

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

K. Tanay, M. A. Saha, and . Hasan, A sampling based method for top-k frequent subgraph mining, Stat. An. & DM, vol.8, issue.4, pp.245-261, 2015.

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

E. Seth, J. Spielman, and . Thill, Social area analysis, data mining, and GIS, Comp. Env. & Urb. Sys, vol.32, issue.2, pp.110-122, 2008.

T. Uno, An Efficient Algorithm for Enumerating Pseudo Cliques, ISAAC 2007, pp.402-414, 2007.
DOI : 10.1007/978-3-540-77120-3_36

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.1510

. Matthijs-van-leeuwen, Maximal exceptions with minimal descriptions, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.259-276, 2010.
DOI : 10.1007/s10618-010-0187-5

J. Wang, J. Cheng, and A. Fu, Redundancy-aware maximal cliques, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.122-130, 2013.
DOI : 10.1145/2487575.2487689