, Frequent Pattern Mining, 2014.

M. Alam, A. Buzmakov, V. Codocedo, and A. Napoli, Mining Definitions from RDF Annotations Using Formal Concept Analysis, Proceedings of IJCAI, pp.823-829, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01186204

M. Alam, A. Buzmakov, and A. Napoli, Exploratory Knowledge Discovery over Web of Data, Discrete Applied Mathematics, vol.249, pp.2-17, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01673439

F. Baader, D. Calvanese, and D. Mcguinness, The Description Logic Handbook, 2003.

A. Belfodil, A. Belfodil, and M. Kaytoue, Anytime Subgroup Discovery in Numerical Domains with Guarantees, Proceedings of ECML-PKDD, vol.11052, pp.500-516, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02117627

A. Bendimerad, M. Plantevit, and C. Robardet, Mining exceptional closed patterns in attributed graphs, Knowledge Information Systems, vol.56, issue.1, pp.1-25, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01625007

K. Bertet, C. Demko, J. Viaud, and C. Guérin, Lattices, closures systems and implication bases: A survey of structural aspects and algorithms, Theoretical Compututer Science, vol.743, pp.93-109, 2018.

. Tijl-de-bie, Subjective Interestingness in Exploratory Data Mining, Proceedings of IDA, vol.8207, pp.19-31, 2013.

H. Blockeel, Data Mining: From Procedural to Declarative Approaches. New Generation Computing, vol.33, pp.115-135, 2015.

J. Ronald, T. Brachman, and . Anand, The Process of Knowledge Discovery in Databases, Advances in Knowledge Discovery and Data Mining, pp.37-57, 1996.

P. Brazdil, C. G. Giraud-carrier, C. Soares, and R. Vilalta, Metalearning -Applications to Data Mining. Cognitive Technologies, 2009.

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Scalable Estimates of Stability, Proceedings of ICFCA, LNAI 8478, pp.157-172, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095920

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Fast Generation of Best Interval Patterns for Nonmonotonic Constraints, Proceedings of ECML-PKDD, vol.9285, pp.157-172
URL : https://hal.archives-ouvertes.fr/hal-01186718

. Springer, , 2015.

C. Carpineto and G. Romano, Concept Data Analysis: Theory and Applications, 2004.

V. Codocedo, I. Lykourentzou, and A. Napoli, A semantic approach to concept lattice-based information retrieval, Annals of Mathematics and Artificial Intelligence, vol.72, pp.169-195, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095859

V. Codocedo and A. Napoli, Formal Concept Analysis and Information Retrieval -A Survey, Proceedings of ICFCA, vol.9113, pp.61-77, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01186196

A. S. Garcez, T. R. Besold, L. De-raedt, P. Földiák, P. Hitzler et al., Neural-Symbolic Learning and Reasoning: Contributions and Challenges, 2015.

T. G. Dietterich, Ensemble Methods in Machine Learning, First International Workshop on Multiple Classifier Systems (MCS), pp.1-15, 2000.

A. Wouter-duivesteijn, A. J. Feelders, and . Knobbe, Exceptional Model Mining -Supervised descriptive local pattern mining with complex target concepts, Data Mining and Knowledge Discovery, vol.30, issue.1, pp.47-98, 2016.

V. Duquenne, Latticial Structures in Data Analysis, Theoretical Computer Science, vol.217, pp.407-436, 1999.

W. Peter, J. Eklund, and . Villerd, A Survey of Hybrid Representations of Concept Lattices in Conceptual Knowledge Processing, Léonard Kwuida and Baris Sertkaya, vol.5986, pp.296-311, 2010.

T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.

B. Ganter and R. Wille, Formal Concept Analysis -Mathematical Foundations, 1999.

B. Ganter and S. O. Kuznetsov, Pattern Structures and Their Projections, Proceedings of ICCS, vol.2120, pp.129-142, 2001.

B. Ganter and S. A. Obiedkov, Conceptual Exploration, 2016.

B. Ganter, G. Stumme, and R. Wille, Formal Concept Analysis, Foundations and Applications, vol.3626, 2005.

N. Grgic-hlaca, M. Bilal-zafar, K. P. Gummadi, and A. Weller, Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning, Proceedings of AAAI-18, pp.51-60, 2018.

D. Grissa, B. Comte, M. Pétéra, E. Pujos-guillot, and A. Napoli, A hybrid and exploratory approach to knowledge discovery in metabolomic data, Discrete Applied Mathematics, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02195463

D. Grissa, B. Comte, E. Pujos-guillot, and A. Napoli, A Hybrid Knowledge Discovery Approach for Mining Predictive Biomarkers in Metabolomic Data, Proceedings of ECML-PKDD, vol.9851, pp.572-587
URL : https://hal.archives-ouvertes.fr/hal-01421011

. Springer, , 2016.

R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Gianotti et al., A Survey of Methods for Explaining Black Box Models, ACM Computing Surveys, vol.51, issue.5, 2018.

M. Hilario, P. Nguyen, H. Do, A. Woznica, and A. Kalousis, Ontology-Based Meta-Mining of Knowledge Discovery Workflows, Meta-Learning in Computational Intelligence, pp.273-315, 2011.

A. Holzinger, M. Dehmer, and I. Jurisica, Knowledge Discovery and interactive Data Mining in Bioinformatics -State-of-the-Art, future challenges and research directions, BMC Bioinformatics, p.1, 2014.

A. Hristoskova, V. Boeva, and E. Tsiporkova, An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis, Third International Conference Information on Technology in Bio-and Medical Informatics (ITBAM), vol.7451, pp.84-98, 2012.

K. Janowicz, J. A. Frank-van-harmelen, P. Hendler, and . Hitzler, Why the Data Train Needs Semantic Rails, vol.36, pp.5-14, 2015.

M. Kaytoue, V. Codocedo, J. Baixeries, and A. Napoli, Three interrelated FCA methods for mining biclusters of similar values on columns, CEUR Workshop Proceedings 1252, pp.243-254, 2014.

M. Kaytoue, V. Codocedo, A. Buzmakov, J. Baixeries, S. O. Kuznetsov et al., Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing, Proceedings of ECML-PKDD, vol.9286, pp.227-231, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01188637

M. Kaytoue, S. O. Kuznetsov, J. Macko, and A. Napoli, Biclustering meets triadic concept analysis, Annals of Mathematics and Artificial Intelligence, vol.70, issue.1-2, pp.55-79, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01101143

M. Kaytoue, S. O. Kuznetsov, A. Napoli, and S. Duplessis, Mining Gene Expression Data with Pattern Structures in Formal Concept Analysis, Information Science, vol.181, issue.10, 1989.
URL : https://hal.archives-ouvertes.fr/hal-00541100

M. Kaytoue, M. Plantevit, and A. Zimmermann, Ahmed Anes Bendimerad, and Céline Robardet. Exceptional contextual subgraph mining. Machine Learning, vol.106, pp.1171-1211, 2017.

S. O. Kuznetsov and T. P. Makhalova, On interestingness measures of formal concepts, Information Sciences, pp.202-219, 2018.

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.

T. P. Makhalova, S. O. Kuznetsov, and A. Napoli, A First Study on What MDL Can Do for FCA, Proceedings of CLA, CEUR Workshop Proceedings 2123, pp.25-36, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01888453

P. Nguyen, M. Hilario, and A. Kalousis, Using Meta-mining to Support Data Mining Workflow Planning and Optimization, Journal of Artificial Intelligence Research (JAIR), vol.51, pp.605-644, 2014.

S. Marco-túlio-ribeiro, C. Singh, and . Guestrin, Why Should I Trust You?": Explaining the Predictions of Any Classifier, Proceedings of SIGKDD, pp.1135-1144, 2016.

M. Rouane-hacene, M. Huchard, A. Napoli, and P. Valtchev, Relational concept analysis: mining concept lattices from multi-relational data, Annals of Mathematics and Artificial Intelligence, vol.67, issue.1, pp.81-108, 2013.
URL : https://hal.archives-ouvertes.fr/lirmm-00816300

O. Sagi and L. Rokach, Ensemble Learning: A Survey, Wiley Interdisciplinary Review on Data Mining and Knowledge Discovery, vol.8, issue.4, 2018.

G. Sourek, V. Aschenbrenner, F. Zelezný, S. Schockaert, and O. Kuzelka, Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures, Journal of Artificial Intelligence Research, vol.62, pp.140-151, 2018.

P. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining, 2018.

S. N. Tran and A. S. Garcez, Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks, IEEE Transactions on Neural Networks and Learning Systems, vol.29, issue.2, pp.246-258, 2018.

J. W. Tukey, Exploratory Data Analysis, 1977.

W. Ugarte, P. Boizumault, B. Crémilleux, A. Lepailleur, S. Loudni et al., Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems, Artificial Intelligence, vol.244, pp.48-69, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02048224

. Matthijs-van-leeuwen, Interactive Data Exploration Using Pattern Mining, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics -State-of-the-Art and Future Challenges, vol.8401, pp.169-182, 2014.

J. Vreeken and N. Tatti, Interesting Patterns, Aggarwal and Han, pp.105-134

Y. Yoneda, M. Sugiyama, and T. Washio, Learning graph representation via formal concept analysis, 2018.

M. Bilal-zafar, I. Valera, M. Gomez-rodriguez, K. P. Gummadi, and A. Weller, From Parity to Preference-based Notions of Fairness in Classification, Proceedings of NIPS, pp.228-238, 2017.

M. J. Zaki and W. Meira, Data Mining and Analysis: Fundamental Concepts and Algorithms, 2014.