L. José, F. Balcázar, and . Dogbey, Evaluation of association rule quality measures through feature extraction, Proceedings of the 12th International Symposium on Advances in Intelligent Data Analysis XII (IDA'2013), vol.8207, pp.68-79, 2013.

F. Benites and E. Sapozhnikova, Hierarchical interestingness measures for association rules with generalization on both antecedent and consequent sides, Pattern Recognition Letters, vol.65, pp.197-203, 2015.

M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery, Data Mining and Knowledge Discovery, vol.31, issue.5, pp.1391-1418, 2017.

A. Cano, A. Zafra, and S. Ventura, An interpretable classification rule mining algorithm, Information Sciences, vol.240, pp.1-20, 2013.

P. Clark and T. Niblett, The CN2 induction algorithm, Machine Learning, vol.3, pp.261-283, 1989.

. William-w-cohen, Fast effective rule induction, Proceedings of the 12th International Conference on Machine Learning (ICML'1995), pp.115-123, 1995.

S. Hugo-jair-escalante, I. Escalera, X. Guyon, Y. Baró, U. Güçlütürk et al., Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning, 2018.

J. Fürnkranz, D. Gamberger, and N. Lavra?, Foundations of Rule Learning, 2012.

J. Fürnkranz, T. Kliegr, and H. Paulheim, On cognitive preferences and the plausability of rule-based models, 2019.

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 2012.

L. Geng and H. J. Hamilton, Interestingness measures for data mining: A survey, ACM Computing Surveys, vol.38, issue.3, 2006.

P. Hájek, M. Holena, and J. Rauch, The GUHA method and its meaning for data mining, Computer System Science, vol.76, issue.1, pp.34-48, 2010.

J. Hills, A. J. Bagnall, B. De-la-iglesia, and G. Richards, BruteSuppression: a size reduction method for apriori rule sets, Intelligent Information Systems, vol.40, issue.3, pp.431-454, 2013.

M. Kaytoue, S. O. Kuznetsov, and A. Napoli, Revisiting numerical pattern mining with formal concept analysis, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'2011), pp.1342-1347, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00600222

T. Kliegr, S. Bahník, and J. Fürnkranz, A review of possible effects of cognitive biases on interpretation of rule-based machine learning models, 2018.

K. Kuratowski, Topology, volume I, 1966.

H. Lakkaraju, S. H. Bach, and J. Leskovec, Interpretable decision sets: A joint framework for description and prediction, Balaji Krishnapuram

D. Aggarwal, R. Shen, and . Rastogi, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2016), pp.1675-1684, 2016.

D. Makinson, General patterns in nonmonotonic reasoning, Artificial Intelligence and Logic Programming, vol.III, 1994.

M. Tom and . Mitchell, Generalization as search, Artificial Intelligence, vol.18, pp.203-226, 1982.

J. Stecher, F. Janssen, and J. Fürnkranz, Shorter rules are better, aren't they? In Toon Calders, Michelangelo Ceci, and Donato Malerba, Proceedings of the 19th International Conference on Discovery Science (DS'2016), vol.9956, pp.279-294, 2016.

T. Wang, C. Rudin, F. Doshi-velez, Y. Liu, E. Klampfl et al., Bayesian rule sets for interpretable classification, 16th IEEE International Conference on Data Mining (ICDM'2016), pp.1269-1274, 2016.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 2016.