R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, ACM SIGMOD Record, vol.27, issue.2, pp.94-105, 1998.
DOI : 10.1145/276305.276314

URL : http://www.cs.cornell.edu/johannes/papers/2005/SubspaceClustering-DAMI.pdf

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of 20th International Conference on Very Large Data Bases (VLDB'94, pp.487-499, 1994.

S. Bickel and T. Scheffer, Multi-View Clustering, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.19-26, 2004.
DOI : 10.1109/ICDM.2004.10095

T. De-bie, Maximum entropy models and subjective interestingness: an application to tiles in binary databases, Data Mining and Knowledge Discovery, vol.1, issue.1-2, pp.407-446, 2011.
DOI : 10.1007/s10115-008-0128-4

E. Galbrun and A. Kimmig, Finding relational redescriptions, Machine Learning, vol.5, issue.3, pp.225-248, 2014.
DOI : 10.14778/2078331.2078332

URL : https://hal.archives-ouvertes.fr/hal-01399271

E. Galbrun and P. Miettinen, From black and white to full color: extending redescription mining outside the Boolean world, Statistical Analysis and Data Mining, vol.4, issue.2, pp.284-303, 2012.
DOI : 10.1145/366573.366611

URL : https://hal.archives-ouvertes.fr/hal-01399211

E. Galbrun and P. Miettinen, Siren, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.1544-1547, 2012.
DOI : 10.1145/2339530.2339776

URL : https://hal.archives-ouvertes.fr/hal-01399213

E. Galbrun and P. Miettinen, Interactive redescription mining, Proceedings of the 2014 ACM SIGMOD international conference on Management of data, SIGMOD '14, pp.1079-1082, 2014.
DOI : 10.1145/2588555.2594520

URL : https://hal.archives-ouvertes.fr/hal-01399276

A. Gallo, P. Miettinen, and H. Mannila, Finding Subgroups having Several Descriptions: Algorithms for Redescription Mining, Proceedings of the 8th SIAM International Conference on Data Mining (SDM'08, pp.334-345, 2008.
DOI : 10.1137/1.9781611972788.30

URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972788.30

J. P. Gray, A corrected ethnographic atlas, World Cultures, vol.10, issue.1, pp.24-85, 1999.

A. J. Grove, J. Y. Halpern, and D. Koller, Random worlds and maximum entropy, [1992] Proceedings of the Seventh Annual IEEE Symposium on Logic in Computer Science, pp.22-33, 1992.
DOI : 10.1109/LICS.1992.185516

URL : http://arxiv.org/pdf/cs/9408101

R. J. Hijmans, S. E. Cameron, L. J. Parra, P. G. Jones, and A. Jarvis, Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology, vol.18, issue.15, 1965.
DOI : 10.1002/joc.1276

URL : http://onlinelibrary.wiley.com/doi/10.1002/joc.1276/pdf

S. Jaroszewicz and D. A. Simovici, Pruning Redundant Association Rules Using Maximum Entropy Principle, Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp.135-147, 2002.
DOI : 10.1007/3-540-47887-6_13

URL : http://www.cs.umb.edu/~sj/biblio/../postscript/PAKDD02web.ps.gz

E. Jaynes, On the rationale of maximum-entropy methods, Proceedings of the IEEE, pp.939-952, 1982.
DOI : 10.1109/PROC.1982.12425

E. T. Jaynes, Probability Theory: The Logic of Science, p.33, 2003.
DOI : 10.1017/CBO9780511790423

F. V. Jensen and F. Jensen, Optimal Junction Trees, Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI'94, pp.360-366, 1994.
DOI : 10.1016/B978-1-55860-332-5.50050-X

J. Kalofolias, E. Galbrun, and P. Miettinen, From Sets of Good Redescriptions to Good Sets of Redescriptions, Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'16, pp.211-220, 2016.
DOI : 10.1109/icdm.2016.0032

URL : https://hal.archives-ouvertes.fr/hal-01399294

K. Kontonasios, D. Bie, and T. , Formalizing Complex Prior Information to Quantify Subjective Interestingness of Frequent Pattern Sets, Proceedings of the 11th International Symposium on Advances in Intelligent Data Analysis (IDA'12, pp.161-171, 2012.
DOI : 10.1007/978-3-642-34156-4_16

K. Kontonasios, D. Bie, and T. , Subjectively interesting alternative clusterings, Machine Learning, vol.22, issue.8, pp.31-56, 2015.
DOI : 10.1109/34.868688

K. Kontonasios, J. Vreeken, D. Bie, and T. , Maximum Entropy Modelling for Assessing Results on Real-Valued Data, 2011 IEEE 11th International Conference on Data Mining, pp.350-359, 2011.
DOI : 10.1109/ICDM.2011.98

K. Kontonasios, J. Vreeken, D. Bie, and T. , Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data, Proceedings of the 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'13), pp.256-271, 2013.
DOI : 10.1007/978-3-642-40991-2_17

URL : http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/660.pdf

P. Kröger and A. Zimek, Encyclopedia of Database Systems, Subspace Clustering Techniques, pp.2873-2875, 2009.

M. Mampaey, N. Tatti, and J. Vreeken, Tell me what i need to know, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.573-581, 2011.
DOI : 10.1145/2020408.2020499

M. Mampaey, J. Vreeken, and N. Tatti, Summarizing data succinctly with the most informative itemsets, ACM Transactions on Knowledge Discovery from Data, vol.6, issue.4, pp.1-1642, 2012.
DOI : 10.1145/2382577.2382580

H. Mannila, D. Pavlov, and P. Smyth, Prediction with Local Patterns Using Crossentropy, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'99, pp.357-361, 1999.
DOI : 10.1145/312129.312281

M. Mihel?i´mihel?i´c and T. , InterSet: Interactive Redescription Set Exploration, Proceedings of the 19th International Conference on Discovery Science (DS'16, pp.35-50, 2016.

A. J. Mitchell-jones, The Atlas of European Mammals, 1999.

G. P. Murdock, Ethnographic Atlas: A Summary, Ethnology, vol.6, issue.2, pp.109-236, 1967.
DOI : 10.2307/3772751

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

L. Parida and N. Ramakrishnan, Redescription Mining: Structure Theory and Algorithms, Proceedings of the 20th National Conference on Artificial Intelligence and the 7th Innovative Applications of Artificial Intelligence Conference (AAAI'05, pp.837-844, 2005.

D. Pavlov, H. Mannila, and P. Smyth, Beyond independence: probabilistic models for query approximation on binary transaction data, IEEE Transactions on Knowledge and Data Engineering, vol.15, issue.6, pp.1409-1421, 2003.
DOI : 10.1109/TKDE.2003.1245281

URL : http://www.datalab.uci.edu/papers/tkde.pdf

S. J. Phillips, R. P. Anderson, and R. E. Schapire, Maximum entropy modeling of species geographic distributions, Ecological Modelling, vol.190, issue.3-4, pp.231-259, 2006.
DOI : 10.1016/j.ecolmodel.2005.03.026

N. Ramakrishnan, D. Kumar, B. Mishra, M. Potts, and R. F. Helm, Turning CARTwheels, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.266-275, 2004.
DOI : 10.1145/1014052.1014083

G. Rasch, Probabilistic models for some intelligence and achievement tests', Copenhagen: Danish Institute for Educational Research, 1960.

N. Tatti, Computational complexity of queries based on itemsets, Information Processing Letters, vol.98, issue.5, pp.183-187, 2006.
DOI : 10.1016/j.ipl.2006.02.003

N. Tatti, Maximum entropy based significance of itemsets, Knowledge and Information Systems, vol.42, issue.8???9, pp.57-77, 2008.
DOI : 10.1007/s10115-008-0128-4

URL : http://users.ics.aalto.fi/ntatti/papers/tatti07rank.pdf

N. Tatti and J. Vreeken, Comparing Apples and Oranges, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.398-413, 2011.
DOI : 10.1145/1081870.1081912

M. Van-leeuwen and E. Galbrun, Association Discovery in Two-View Data, IEEE Transactions on Knowledge and Data Engineering, vol.27, issue.12, pp.3190-3202, 2015.
DOI : 10.1109/TKDE.2015.2453159

URL : https://hal.archives-ouvertes.fr/hal-01242988

J. Vreeken and M. Van-leeuwen, Krimp: mining itemsets that compress, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.169-214, 2011.
DOI : 10.1001/jama.1961.03040290005002

C. Wang and S. Parthasarathy, Summarizing itemset patterns using probabilistic models, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.730-735, 2006.
DOI : 10.1145/1150402.1150495

URL : http://www.cse.ohio-state.edu/~wachao/KDD06_Wang.pdf

H. Wu, J. Vreeken, N. Tatti, and N. Ramakrishnan, Uncovering the plot: detecting surprising coalitions of entities in multi-relational schemas, Data Mining and Knowledge Discovery, vol.17, issue.4, pp.5-6, 2014.
DOI : 10.1109/TKDE.2005.60