Bibliography

Major publications by the team in recent years

• 1M. Gebser, T. Guyet, R. Quiniou, J. Romero, T. Schaub.
  
  Knowledge-based Sequence Mining with ASP, in: IJCAI 2016- 25th International joint conference on artificial intelligence, New-york, United States, AAAI, July 2016, 8 p.
  
  https://hal.inria.fr/hal-01327363
• 2A. Siffer, P.-A. Fouque, A. Termier, C. Largouët.
  
  Anomaly Detection in Streams with Extreme Value Theory, in: KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, August 2017. [ DOI : 10.1145/3097983.3098144 ]
  
  https://hal.archives-ouvertes.fr/hal-01640325

Publications of the year

Articles in International Peer-Reviewed Journals

• 3T. Bouadi, M.-O. Cordier, P. Moreau, R. Quiniou, J. Salmon-Monviola, C. Gascuel-Odoux.
  
  A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management, in: Environmental Modelling and Software, November 2017, vol. 97, pp. 229 - 242. [ DOI : 10.1016/j.envsoft.2017.07.019 ]
  
  https://hal.inria.fr/hal-01597840
• 4E. Drezen, T. Guyet, A. Happe.
  
  From medico-administrative databases analysis to care trajectories analytics: an example with the French SNDS, in: Fundamental and Clinical Pharmacology, September 2017. [ DOI : 10.1111/fcp.12323 ]
  
  https://hal.inria.fr/hal-01631802
• 5C. Largouët, O. Krichen, Y. Zhao.
  
  Extended Automata for Temporal Planning of Interacting Agents, in: International Journal of Monitoring and Surveillance Technologies Research, April 2017, vol. 5, n^o 1, pp. 30 - 48. [ DOI : 10.4018/IJMSTR.2017010102 ]
  
  https://hal-univ-rennes1.archives-ouvertes.fr/hal-01640137
• 6V. Leroy, M. Kirchgessner, A. Termier, S. Amer-Yahia.
  
  TopPI: An efficient algorithm for item-centric mining, in: Information Systems, 2017, vol. 64, pp. 104 - 118. [ DOI : 10.1016/j.is.2016.09.001 ]
  
  https://hal.archives-ouvertes.fr/hal-01479067

International Conferences with Proceedings

• 7Y. Dauxais, D. Gross-Amblard, T. Guyet, A. Happe.
  
  Extraction de chroniques discriminantes, in: Extraction et Gestion des Connaissances (EGC), Grenoble, France, January 2017.
  
  https://hal.inria.fr/hal-01413473
• 8Y. Dauxais, T. Guyet, D. Gross-Amblard, A. Happe.
  
  Discriminant chronicles mining: Application to care pathways analytics, in: Artificial Intelligence in Medicine, Vienna, Austria, 16th Conference on Artificial Intelligence in Medicine, June 2017, https://arxiv.org/abs/1709.03309. [ DOI : 10.1007/978-3-319-59758-4₂6 ]
  
  https://hal.archives-ouvertes.fr/hal-01568929
• 9C. Gautrais, P. Cellier, R. Quiniou, A. Termier.
  
  Topic Signatures in Political Campaign Speeches, in: EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, September 2017.
  
  https://hal.archives-ouvertes.fr/hal-01640498
• 10C. Gautrais, R. Quiniou, P. Cellier, T. Guyet, A. Termier.
  
  Purchase Signatures of Retail Customers, in: PAKDD 2017 - The Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, South Korea, Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2017.
  
  https://hal.archives-ouvertes.fr/hal-01639795
• 11M. Guilleme, L. Rozé, V. Masson, C. Carton, R. Quiniou, A. Termier.
  
  Improving time-series rule matching performance for detecting energy consumption patterns, in: DARE 2017 - 5th International Workshop on Data Analytics for Renewable Energy Integration, Skopje, Macedonia, Springer, September 2017, vol. 10691, pp. 59-71. [ DOI : 10.1007/978-3-319-71643-5_6 ]
  
  https://hal.inria.fr/hal-01654890
• 12T. Guyet, A. Happe, Y. Dauxais.
  
  Declarative Sequential Pattern Mining of Care Pathways, in: Conference on Artificial Intelligence in Medicine in Europe, Vienna, Austria, 16th Conference on Artificial Intelligence in Medicine, June 2017, vol. 24, pp. 1161 - 266, https://arxiv.org/abs/1707.08342. [ DOI : 10.1007/978-3-319-59758-4_29 ]
  
  https://hal.inria.fr/hal-01569023
• 13T. Guyet, R. Quiniou, V. Masson.
  
  Mining relevant interval rules, in: International Conference on Formal Concept Analysis, Rennes, France, Supplementary proceedings of International Conference on Formal Concept Analysis (ICFCA), June 2017, https://arxiv.org/abs/1709.03267.
  
  https://hal.inria.fr/hal-01584981
• 14S. Kelly.
  
  A Communication Model that Bridges Knowledge Delivery between Data Miners and Domain Users , in: 51th Hawaii International Conference on System Sciences (HICSS ), Hawaii, United States, January 2018.
  
  https://hal.archives-ouvertes.fr/hal-01651737
• 15A. Samet, T. Guyet, B. Negrevergne, T.-T. Dao, T. Nha Hoang, M.-C. Ho Ba Tho.
  
  Expert Opinion Extraction from a Biomedical Database, in: Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lugano, Switzerland, Proceedings of 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Springer, July 2017, vol. 31, n^o LNCS 10369, pp. 1 - 12, https://arxiv.org/abs/1709.03270. [ DOI : 10.1016/S0888-613X(02)00066-X ]
  
  https://hal.inria.fr/hal-01584984
• 16A. Siffer, P.-A. Fouque, A. Termier, C. Largouët.
  
  Anomaly Detection in Streams with Extreme Value Theory, in: KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, August 2017. [ DOI : 10.1145/3097983.3098144 ]
  
  https://hal.archives-ouvertes.fr/hal-01640325
• 17K. Tsesmeli, M. Boumghar, T. Guyet, R. Quiniou, L. Pierre.
  
  Fouille de motifs temporels négatifs, in: EGC 2018 - 18ème Conférence Internationale sur l'Extraction et la Gestion des Connaissances, Paris, France, January 2018, pp. 1-6.
  
  https://hal.inria.fr/hal-01657540

Conferences without Proceedings

• 18P. Besnard, T. Guyet, V. Masson.
  
  Admissible generalizations of examples as rules, in: 11e Journées d'Intelligence Artificielle Fondamentale, Caen, France, July 2017.
  
  https://hal.archives-ouvertes.fr/hal-01576047
• 19C. Gautrais, Y. Dauxais, M. Guilleme.
  
  Multi-Plant Photovoltaic Energy Forecasting Challenge: Second place solution, in: Discovery Challenges co-located with European Conference on Machine Learning - Principle and Practice of Knowledge Discovery in Database, Skopje, Macedonia, September 2017.
  
  https://hal.archives-ouvertes.fr/hal-01639813
• 20A. Samet, T. Guyet, B. Negrevergne.
  
  Mining rare sequential patterns with ASP, in: ILP 2017 - 27th International Conference on Inductive Logic Programming, Orléans, France, September 2017.
  
  https://hal.archives-ouvertes.fr/hal-01569582

Scientific Books (or Scientific Book chapters)

• 21M.-O. Cordier, P. Dague, Y. Pencolé, L. Travé-Massuyès.
  
  Diagnosis and supervision: model-based approaches, in: A guided tour of artificial intelligence research, H. P. Pierre Marquis (editor), Knowledge representation and reasoning, Springer, 2018, n^o 1.
  
  https://hal.archives-ouvertes.fr/hal-01483436
• 22T. Guyet, Y. Moinard, R. Quiniou, T. Schaub.
  
  Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks, in: Advances in Knowledge Discovery and Management, B. Pinaud, F. Guillet, B. Cremilleux, C. de Runz (editors), Springer, October 2017, vol. 7, pp. 41–81, https://arxiv.org/abs/1711.05090.
  
  https://hal.inria.fr/hal-01631879
• 23A. Napoli, A. Termier.
  
  La fouille de données, in: Les Big Data à découvert, M. Bouzeghoub, R. Mosseri (editors), CNRS Editions, 2017, pp. 1-3.
  
  https://hal.inria.fr/hal-01673437

Scientific Popularization

• 24M.-O. Cordier.
  
  Regard sur « Le mythe de la Singularité. Faut-il craindre l'intelligence artificielle ? », in: Interstices, June 2017.
  
  https://hal.inria.fr/hal-01616345
• 25A. Termier, J. Jongwane.
  
  Vers une démocratisation des outils pour l'exploration de données ?, in: Interstices, December 2017.
  
  https://hal.inria.fr/hal-01688785

References in notes

• 26L. Breiman.
  
  Bagging predictors, in: Machine Learning, Aug 1996, vol. 24, n^o 2, pp. 123–140.
  
  https://doi.org/10.1007/BF00058655
• 27S. Colas, C. Collin, P. Piriou, M. Zureik.
  
  Association between total hip replacement characteristics and 3-year prosthetic survivorship: A population-based study, in: JAMA Surgery, 2015, vol. 150, n^o 10, pp. 979–988.
• 28G. C. Garriga, P. Kralj, N. Lavrac.
  
  Closed Sets for Labeled Data, in: Journal of Machine Learning Research, 2008, vol. 9, pp. 559–580.
  
  http://doi.acm.org/10.1145/1390681.1390700
• 29M. Kaytoue, S. O. Kuznetsov, A. Napoli.
  
  Revisiting numerical pattern mining with formal concept analysis, in: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 2011, vol. 22, n^o 1, 1342 p.
• 30N. Landwehr, M. Hall, E. Frank.
  
  Logistic model trees, in: Machine learning, 2005, vol. 59, n^o 1-2, pp. 161–205.
• 31G. Moulis, M. Lapeyre-Mestre, A. Palmaro, G. Pugnet, J.-L. Montastruc, L. Sailler.
  
  French health insurance databases: What interest for medical research?, in: La Revue de Médecine Interne, 2015, vol. 36, n^o 6, pp. 411 - 417.
• 32E. Nowak, A. Happe, J. Bouget, F. Paillard, C. Vigneau, P.-Y. Scarabin, E. Oger.
  
  Safety of Fixed Dose of Antihypertensive Drug Combinations Compared to (Single Pill) Free-Combinations: A Nested Matched Case–Control Analysis, in: Medicine, 2015, vol. 94, n^o 49, e2229 p.
• 33E. Polard, E. Nowak, A. Happe, A. Biraben, E. Oger.
  
  Brand name to generic substitution of antiepileptic drugs does not lead to seizure-related hospitalization: a population-based case-crossover study, in: Pharmacoepidemiology and drug safety, 2015, vol. 24, n^o 11, pp. 1161–1169.
• 34R. E. Schapire.
  
  The Boosting Approach to Machine Learning: An Overview, Springer New York, New York, NY, 2003, pp. 149–171.
• 35M. Shokoohi-Yekta, Y. Chen, B. Campana, B. Hu, J. Zakaria, E. Keogh.
  
  Discovery of meaningful rules in time series, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2015, pp. 1085–1094.
• 36R. Wang, Y.-L. He, C.-Y. Chow, F.-F. Ou, J. Zhang.
  
  Learning ELM-tree from big data based on uncertainty reduction, in: Fuzzy Sets and Systems, 2015, vol. 258, pp. 79–100.