Mehdi Kaytoue

Associate Professor in Computer Science at INSA Lyon | PhD (2011) - HDR (2020)

Currently on sabbatical from INSA Lyon, working as R&I Manager at Infologic since February 2018.

Google Scholar • DBLP • ORCID • HAL • ResearchGate • Publons • LinkedIn • X (Twitter)

About Me

Profile Picture

I am an associate professor of Computer Science at the Institut National des Sciences Appliquées since 2012 (INSA de Lyon, France). My main research interests are artificial intelligence, data-mining, and especially pattern mining with formal concept analysis. Original methods and algorithms developped as part of my research are applied to various domains with a particular attention to software engineering, digitalization, olfaction in neuroscience, geo-located social media analysis, electronic sports and video game analytics. I bring extensive industry experience, having participated in several research projects and collaborations, including CIFRE, FUI, and EU FP7 Marie Curie. Additionally, I have a year of experience as an auto-entrepreneur, eight months in a startup focusing on knowledge transfer, and most notably, seven years working for a French PME software editor. During this time, I have developed a robust skill set and gained valuable expertise.

Artificial Intelligence Knowledge Discovery Data Mining Formal Concept Analysis Pattern Mining Subgroup Discovery Applications Industry

Experience

Research

My scientific adventure began with the study of a binary relationship, very often illustrated by grocery store transaction data, linking customers and products they buy. How to make this relationship speak? What knowledge, behavioral habits, recommendations, etc. can we characterize? This initial question allowed me to travel through different application fields (biology, neuroscience, social networks and video games analytics, software engineering), seeking to implement or adapt data mining methods to try to understand some phenomena while properly formalizing data and patterns in the most rigorous way. My research accordingly follows three main axes: the formalism framing the methods (Formal Concept Analysis), the methodological and algorithmic aspects related in Data mining, and finally the Knowledge Discovery "in practice" through several concrete applications encountered during collaborations with other scientists or industrial partners.

▶ HDR: Contributions to Pattern Discovery and Formal Concept Analysis (2020) 
Knowledge Discovery Formal Concept Analysis Subgroup Discovery Pattern Mining Applications

Jury

  • President: Angela Bonifati
  • Examiners: Michael Berthold (Knime.org), Johannes Fürnkranz, Amedeo Napoli, Jean-François Boulicat
  • Reviewers: Karell Berthet, Florent Masseglia, Christel Vrain

More details on HAL

Abstract

The process of collecting and analyzing data to answer predictive, explanatory, and decision-making issues has come to be known as "data science" for more than thirty years. Firstly used only by scientists, mainly by statisticians, the term is now widely used in the academics and industrial world. This can be explained in two ways: (i) data is ubiquitous, large, and varied, and (ii) there has been an awareness of the omniscient potential of data. The latter can be economic, societal, scientific, or related to health-care, and is based not only on the data that an entity has, but also on data that it can get (sensors, social networks, open data, etc., freely or not) making the data a black oil that still needs algorithms, methods and methodologies, to be properly refined. One component of data science, Knowledge Discovery in databases (KDD), deals in particular with the Data-Information-Knowledge process with the aim of explaining relationships or discovering hidden properties. Opposed to a purely statistical approach, a family of methods has met an important success over the last twenty years: data-mining and especially pattern-mining. Their goal is to describe, summarize, raise hypotheses from data. In particular, pattern mining makes it possible to efficiently find regularities of various types (such as frequent patterns in a set of transactions, molecular sub-graphs characteristic of toxicity, locally co-expressed gene groups, etc.). In fact, where conventional approaches aim to validate or invalidate an hypothesis given a priori, the search of patterns is seen as an enumeration technique of all the possible hypotheses (a set of exponential size w.r.t the input data) verifying some given constraints or maximizing a certain interest for the expert. Once discovered, the best hypotheses can then be tested, validated or invalidated and ultimately validated as knowledge unit. My scientific adventure began with the study of a binary relationship, very often illustrated by grocery store transaction data, linking customers and products they buy. How to make this relationship speak? What knowledge, behavioral habits, recommendations, etc. can we characterize? This initial question allowed me to travel through different application fields (biology, neuroscience, social networks and video games analytics), seeking to implement or adapt data mining methods to try to understand some phenomena while properly formalizing data and patterns in the most rigorous way. This is the story of this manuscript, according to three main research axes: the formalism framing the methods (Formal Concept Analysis), the methodological and algorithmic aspects related in Data mining, and finally the Knowledge Discovery "in practice" through several concrete applications encountered during collaborations with other scientists or industrial partners.

▶ PhD: Mining numerical data with formal concept analysis and pattern structures (2011) 
Formal Concept Analysis Numerical data Pattern mining Biclustering Information fusion Gene expression analysis

Supervisors : Amedeo Napoli, Sébastien Duplessis

Jury

  • President: Bernard Girau
  • Examiners: Sergei O. Kuznetsov, Céline Rouveirol
  • Reviewers: Jean-François Boulicaut, Bernhard Ganter

More details on theses.fr

Abstract

The main topic of this thesis addresses the important problem of mining numerical data, and especially gene expression data. These data characterize the behaviour of thousand of genes in various biological situations (time, cell, etc.).A difficult task consists in clustering genes to obtain classes of genes with similar behaviour, supposed to be involved together within a biological process.Accordingly, we are interested in designing and comparing methods in the field of knowledge discovery from biological data. We propose to study how the conceptual classification method called Formal Concept Analysis (FCA) can handle the problem of extracting interesting classes of genes. For this purpose, we have designed and experimented several original methods based on an extension of FCA called pattern structures. Furthermore, we show that these methods can enhance decision making in agronomy and crop sanity in the vast formal domain of information fusion.

International or large-scale projects

Regional or small-scale projects

Responsibilities

Invited talks

Evaluation committees

Vulgarisation, dissemination

PhD Supervision & Alumni

Ongoing PhD's

Completed PhD's

▶ Youcef Remil: A data mining perspective on explainable AIOps with applications to software maintenance (2023) 
Software Engineering & AIOps Predictive maintenance CIFRE with the company INFOLOGIC

Supervisors : Jean-François Boulicaut, Mehdi Kaytoue

Jury

  • President: Christel Vrain
  • Examiners: Arnaud Soulet, Peggy Cellier, Ahmed Anes Bendimerad
  • Reviewers: Romain Robbes, Alexandre Termier

More details on theses.fr

Abstract

The genuine supervision of modern IT systems presents new challenges in terms of scalability, reliability, and efficiency. Traditional operations and maintenance systems that rely on manual tasks and individual troubleshooting are inefficient. Rule-based inference engines, although useful for detecting anomalies and automating resolution, are limited in handling the large number of alerts generated by IT systems. Artificial Intelligence for Operating Systems (AIOps) proposes the use of advanced analytics and machine learning to improve and automate supervision systems. However, there are several challenges in this field. Firstly, the lack of unified terminology makes it difficult to compare contributions from different disciplines. The requirements and metrics for constructing effective AIOps models are not well-defined. Secondly, AIOps has primarily focused on predictive models for anomaly detection and failure prediction, neglecting descriptive models that can handle data quality and complexity concerns. Thirdly, the reliance on opaque black box models limits their adoption by industry practitioners who need a clear understanding of the decision-making process of maintenance models. Lastly, existing AIOps solutions often overlook performance evaluation and scalability issues when developing and evaluating incident management models. As part of this Ph.D. thesis, we propose several contributions to tackle these challenges more effectively. Firstly, we offer a systematic approach to AIOps that organizes the extensive knowledge surrounding it. By categorizing data-driven approaches from various research areas and disciplines according to industry standards and requirements, we provide a cohesive framework. Secondly, we explore the application of Subgroup Discovery and its generalization Exceptional Model Mining, a promising data mining technique, in the context of AIOps. This well-defined framework allows for the extraction of valuable hypotheses from large and diverse datasets. It enables users to understand, interact with, and interpret the underlying processes behind predictive models. Our contributions in this area include a practical application focused on identifying suspicious query fragments in large SQL workloads to pinpoint performance degradation issues. Additionally, we develop an interpretation mechanism for incident triage models, providing contextualized explanations for the model's decisions. Furthermore, we address the challenging problem of memory Java analysis using huge and complex datasets that incorporate hierarchical data. Lastly, we address the issue of scalability by studying incident deduplication, a well-known problem in the industry. Our goal is to efficiently retrieve the most similar crash reports by combining locality-sensitive hashing and learning-to-hash techniques within a unified framework.

▶ Romain Mathonat: Rule discovery in labeled sequential data : Application to game analytics (2020) 
Sequential pattern mining MCTS / Anytime algorithms Vertical farms / Game Analytics CIFRE with the company ATOS

Supervisors : Jean-François Boulicaut, Mehdi Kaytoue

Jury

  • President: Sihem Amer-Yahia
  • Examiners: Chedy Raïssi, Anne Laurent, Germain Forestier
  • Reviewers: Alexandre Termier, Martin Atzmüller

More details on theses.fr

Abstract

It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of rules covering sets of objects having interesting properties, e.g., they characterize a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering rules within labeled sequential data has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. In this thesis, we propose to apply bandit models and Monte Carlo Tree Search to explore the search space of possible rules using an exploration-exploitation trade-off, on different data types such as sequences of itemset or time series. For a given budget, they find a collection of top-k best rules in the search space w.r.t chosen quality measure. They require a light configuration and are independent from the quality measure used for pattern scoring. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework has been exploited in a sequential data mining setting. We have conducted thorough and comprehensive evaluations of our algorithms on several datasets to illustrate their added-value, and we discuss their qualitative and quantitative results. To assess the added-value of one or our algorithms, we propose a use case of game analytics, more precisely Rocket League match analysis. Discovering interesting rules in sequences of actions performed by players and using them in a supervised classification model shows the efficiency and the relevance of our approach in the difficult and realistic context of high dimensional data. It supports the automatic discovery of skills and it can be used to create new game modes, to improve the ranking system, to help e-sport commentators, or to better analyse opponent teams, for example.

▶ Aimene Belfodil: An order theoretic point-of-view on subgroup discovery (2020) 
Formal Concept Analysis Order Theory Subgroup discovery CIFRE with the munic.io company

Supervisors : Mehdi Kaytoue, Céline Robardet

Jury

  • President: Miguel Couciero
  • Examiners: Peggy Cellier, Arno Siebes
  • Reviewers: Bruno Crémilleux, Bernhard Ganter

More details on theses.fr

Abstract

As the title of this dissertation may suggest, the aim of this thesis is to provide an order-theoretic point of view on the task of subgroup discovery. Subgroup discovery is the automatic task of discovering interesting hypotheses in databases. That is, given a database, the hypothesis space the analyst wants to explore and a formal way of how the analyst gauges the quality of the hypotheses (e.g. a quality measure); the automated task of subgroup discovery aims to extract the interesting hypothesis w.r.t. these parameters. In order to elaborate fast and efficient algorithms for subgroup discovery, one should understand the underlying properties of the hypothesis space on the one hand and the properties of its quality measure on the other. In this thesis, we extend the state-of-the-art by: (i) providing a unified view of the hypotheses space behind subgroup discovery using the well-founded mathematical tool of order theory, (ii) proposing the new hypothesis space of conjunction of linear inequalities in numerical databases and the algorithms enumerating its elements and (iii) proposing an anytime algorithm for discriminative subgroup discovery on numerical datasets providing guarantees upon interruption.

▶ Guillaume Bosc: Anytime discovery of a diverse set of patterns with Monte Carlo tree search (2017) 
Subgroup Discovery Monte Carlo Tree Search Classification Rules Scents & odors perception

Supervisors : Jean-François Boulicaut, Mehdi Kaytoue

Jury

  • President: Sihem Amer Yahia
  • Examiners: Moustafa Bensafi, Peter Flach, Katharina Morik
  • Reviewers: Toon Calders, Tristan Cazenave

More details on theses.fr

Abstract

The discovery of patterns that strongly distinguish one class label from another is still a challenging data-mining task. Subgroup Discovery (SD) is a formal pattern mining framework that enables the construction of intelligible classifiers, and, most importantly, to elicit interesting hypotheses from the data. However, SD still faces two major issues: (i) how to define appropriate quality measures to characterize the interestingness of a pattern; (ii) how to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is unfeasible. The first issue has been tackled by Exceptional Model Mining (EMM) for discovering patterns that cover tuples that locally induce a model substantially different from the model of the whole dataset. The second issue has been studied in SD and EMM mainly with the use of beam-search strategies and genetic algorithms for discovering a pattern set that is non-redundant, diverse and of high quality. In this thesis, we argue that the greedy nature of most such previous approaches produces pattern sets that lack diversity. Consequently, we formally define pattern mining as a game and solve it with Monte Carlo Tree Search (MCTS), a recent technique mainly used for games and planning problems in artificial intelligence. Contrary to traditional sampling methods, MCTS leads to an any-time pattern mining approach without assumptions on either the quality measure or the data. It converges to an exhaustive search if given enough time and memory. The exploration/exploitation trade-off allows the diversity of the result set to be improved considerably compared to existing heuristics. We show that MCTS quickly finds a diverse pattern set of high quality in our application in neurosciences. We also propose and validate a new quality measure especially tuned for imbalanced multi-label data.

▶ Olivier Cavadenti: Contribution to unitary traces analysis with pattern discovery (2016) 
Subgroup Discovery Manufacturing products & tracability Pattern mining with expert models CIFRE with Courbon/Actemium (Vinci Group)

Supervisors : Jean-François Boulicaut, Mehdi Kaytoue

Jury

  • President: Pascal Poncelet
  • Examiners: François Brucker, Frédéric Menut (Actemium), Chedy Raissi
  • Reviewers: Jérôme Aze, Alexandre Termier

More details on theses.fr

Abstract

In a manufacturing context, a product is moved through different placements or sites before it reaches the final customer. Each of these sites have different functions, e.g. creation, storage, retailing, etc. In this scenario, traceability data describes in a rich way the events a product undergoes in the whole supply chain (from factory to consumer) by recording temporal and spatial information as well as other important elements of description. Thus, traceability is an important mechanism that allows discovering anomalies in a supply chain, like diversion of computer equipment or counterfeits of luxury items. In this thesis, we propose a methodological framework for mining unitary traces using knowledge discovery methods. We show how the process of data mining applied to unitary traces encoded in specific data structures allows extracting interesting patterns that characterize frequent behaviors. We demonstrate that domain knowledge, that is the flow of products provided by experts and compiled in the industry model, is useful and efficient for classifying unitary traces as deviant or not. Moreover, we show how data mining techniques can be used to provide a characterization for abnormal behaviours (When and how did they occur?). We also propose an original method for detecting identity usurpations in the supply chain based on behavioral data, e.g. distributors using fake identities or concealing them. We highlight how the knowledge discovery in databases, applied to unitary traces encoded in specific data structures (with the help of expert knowledge), allows extracting interesting patterns that characterize frequent behaviors. Finally, we detail the achievements made within this thesis with the development of a platform of traces analysis in the form of a prototype.

Post-docs and research engineers

Master's research students

Alumnis

(constitution in progress...)

Teaching

INSA Lyon: 2012-2018

I mainly taught artificial intelligence to fourth and fifth year students (INSA Lyon, CS department) and to a class of international students. More details soon.

Attaché temporaire d'enseignement et de recherche (ATER, 2010-2011)96h

Département informatique de la faculté des sciences et technologies de l'université Henri Poincaré (Nancy 1) lors du premier semestre de ma dernière année de préparation de thèse.

Enseignements au CNAM100h

Le conservatoire national des arts et metiers propose une formation riche dans divers domaines, afin de permettre à ses auditeurs une ré-insertion professionnelle, une mise-à-niveau des connaissances pour une possible promotion professionnelle, ou simplement pour repondre à l'intérêt de chacun. Elle prépare au titre d'ingénieur du CNAM, diplôme reconnu. J'ai dispensé pendant deux ans l'enseignement d'intelligence artificielle dans le dernier cycle du parcours. Ce cours de 50 heures par an introduit les problematiques de l'intelligence artificielle et différentes manières de formaliser et resoudre des problèmes. Merci à Amedeo Napoli pour les supports de qualité qu'il m'avait transmis.

Moniteur au centre d'initiation à l'enseignement supérieur (CIES, 2008-2010)196h

Université Nancy 2, UFR de mathématiques et informatique.

Publications

International journals

[1]
R. Mathonat, D. Nurbakova, J.-F. Boulicaut, et M. Kaytoue, « Anytime mining of sequential discriminative patterns in labeled sequences », Knowl. Inf. Syst., vol. 63, no 2, p. 439‑476, 2021, doi: 10.1007/S10115-020-01523-7.
[2]
A. Belfodil, S. O. Kuznetsov, et M. Kaytoue, « On pattern setups and pattern multistructures », Int. J. Gen. Syst., vol. 49, no 8, p. 785‑818, 2020, doi: 10.1080/03081079.2020.1806832.
[3]
C. C. Licon et al., « Chemical features mining provides new descriptive structure-odor relationships », PLOS Computational Biology, vol. 15, no 4, p. e1006945, avr. 2019, doi: 10.1371/journal.pcbi.1006945.
[4]
G. Bosc, J.-F. Boulicaut, C. Raïssi, et M. Kaytoue, « Anytime discovery of a diverse set of patterns with Monte Carlo tree search », Data Min. Knowl. Discov., vol. 32, no 3, p. 604‑650, 2018, doi: 10.1007/s10618-017-0547-5.
[5]
J. Baixeries, V. Codocedo, M. Kaytoue, et A. Napoli, « Characterizing approximate-matching dependencies in formal concept analysis with pattern structures », Discrete Applied Mathematics, vol. 249, p. 18‑27, 2018, doi: 10.1016/j.dam.2018.03.073.
[6]
M. Kaytoue, M. Plantevit, A. Zimmermann, A. A. Bendimerad, et C. Robardet, « Exceptional contextual subgraph mining », Machine Learning, vol. 106, no 8, p. 1171‑1211, 2017, doi: 10.1007/s10994-016-5598-0.
[7]
G. Bosc, P. Tan, J.-F. Boulicaut, C. Raïssi, et M. Kaytoue, « A Pattern Mining Approach to Study Strategy Balance in RTS Games », IEEE Trans. Comput. Intellig. and AI in Games, vol. 9, no 2, p. 123‑132, 2017, doi: 10.1109/TCIAIG.2015.2511819.
[8]
M. Kaytoue, Y. Pitarch, M. Plantevit, et C. Robardet, « What effects topological changes in dynamic graphs? - Elucidating relationships between vertex attributes and the graph structure », Social Netw. Analys. Mining, vol. 5, no 1, p. 55:1-55:17, 2015, doi: 10.1007/s13278-015-0294-9.
[9]
M. Kaytoue, S. O. Kuznetsov, J. Macko, et A. Napoli, « Biclustering meets triadic concept analysis », Ann. Math. Artif. Intell., vol. 70, no 1‑2, p. 55‑79, 2014, doi: 10.1007/s10472-013-9379-1.
[10]
J. Baixeries, M. Kaytoue, et A. Napoli, « Characterizing functional dependencies in formal concept analysis with pattern structures », Ann. Math. Artif. Intell., vol. 72, no 1‑2, p. 129‑149, 2014, doi: 10.1007/s10472-014-9400-3.
[11]
M. Kaytoue, S. O. Kuznetsov, A. Napoli, et S. Duplessis, « Mining gene expression data with pattern structures in formal concept analysis », Inf. Sci., vol. 181, no 10, p. 1989‑2001, 2011, doi: 10.1016/j.ins.2010.07.007.

International Conferences

[1]
Y. Remil, A. Bendimerad, R. Mathonat, C. Raïssi, and M. Kaytoue, ‘DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection’, in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024, ACM, 2024, p. 198:1-198:12. doi: 10.1145/3597503.3639146.
[2]
A. Bendimerad, R. Mathonat, Y. Remil, and M. Kaytoue, ‘Exploiting Formal Concept Analysis for Data Modeling in Data Lakes’, in Conceptual Knowledge Structures - First International Joint Conference, CONCEPTS 2024, Cádiz, Spain, September 9-13, 2024, Proceedings, I. P. Cabrera, S. Ferré, and S. A. Obiedkov, Eds., in Lecture Notes in Computer Science, vol. 14914. Springer, 2024, pp. 270–285. doi: 10.1007/978-3-031-67868-4_18.
[3]
J. Baixeries, V. Codocedo, M. Kaytoue, and A. Napoli, ‘Dependency Covers from an FCA Perspective’, in Proceedings of the 11th International Workshop ‘What can FCA do for Artificial Intelligence?’ co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R. China; August 20, 2023, S. O. Kuznetsov, A. Napoli, and S. Rudolph, Eds., in CEUR Workshop Proceedings, vol. 3489. CEUR-WS.org, 2023, pp. 57–68. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-3489/paper6.pdf
[4]
Y. Remil, A. Bendimerad, M. Plantevit, C. Robardet, and M. Kaytoue, ‘Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery’, in 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021, Porto, Portugal, October 6-9, 2021, IEEE, 2021, pp. 1–10. doi: 10.1109/DSAA53316.2021.9564164.
[5]
Y. Remil, A. Bendimerad, R. Mathonat, P. Chaleat, and M. Kaytoue, ‘“What makes my queries slow?”: Subgroup Discovery for SQL Workload Analysis’, in 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15-19, 2021, IEEE, 2021, pp. 642–652. doi: 10.1109/ASE51524.2021.9678915.
[6]
R. Mathonat, D. Nurbakova, J.-F. Boulicaut, and M. Kaytoue, ‘Anytime Subgroup Discovery in High Dimensional Numerical Data’, in 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021, Porto, Portugal, October 6-9, 2021, IEEE, 2021, pp. 1–10. doi: 10.1109/DSAA53316.2021.9564223.
[7]
R. Mathonat, J.-F. Boulicaut, and M. Kaytoue, ‘A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League’, in IEEE Conference on Games, CoG 2020, Osaka, Japan, August 24-27, 2020, IEEE, 2020, pp. 267–274. doi: 10.1109/COG47356.2020.9231739.
[8]
R. Mathonat, D. Nurbakova, J.-F. Boulicaut, and M. Kaytoue, ‘SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences’, in 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, DC, USA, October 5-8, 2019, L. Singh, R. D. D. Veaux, G. Karypis, F. Bonchi, and J. Hill, Eds., IEEE, 2019, pp. 81–90. doi: 10.1109/DSAA.2019.00022.
[9]
V. Codocedo, J. Baixeries, M. Kaytoue, and A. Napoli, ‘Sampling Representation Contexts with Attribute Exploration’, in Formal Concept Analysis - 15th International Conference, ICFCA 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings, D. Cristea, F. L. Ber, and B. Sertkaya, Eds., in Lecture Notes in Computer Science, vol. 11511. Springer, 2019, pp. 307–314. doi: 10.1007/978-3-030-21462-3_20.
[10]
A. Belfodil, A. Belfodil, and M. Kaytoue, ‘Mining Formal Concepts Using Implications Between Items’, in Formal Concept Analysis - 15th International Conference, ICFCA 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings, D. Cristea, F. L. Ber, and B. Sertkaya, Eds., in Lecture Notes in Computer Science, vol. 11511. Springer, 2019, pp. 173–190. doi: 10.1007/978-3-030-21462-3_12.
[11]
A. Belfodil et al., ‘FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery’, in 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, DC, USA, October 5-8, 2019, L. Singh, R. D. D. Veaux, G. Karypis, F. Bonchi, and J. Hill, Eds., IEEE, 2019, pp. 91–99. doi: 10.1109/DSAA.2019.00023.
[12]
V. Codocedo, J. Baixeries, M. Kaytoue, and A. Napoli, ‘Characterizing Covers of Functional Dependencies using FCA’, in Proceedings of the Fourteenth International Conference on Concept Lattices and Their Applications, CLA 2018, Olomouc, Czech Republic, June 12-14, 2018, D. I. Ignatov and L. Nourine, Eds., in CEUR Workshop Proceedings, vol. 2123. CEUR-WS.org, 2018, pp. 279–290. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-2123/paper23.pdf
[13]
A. Belfodil, S. O. Kuznetsov, and M. Kaytoue, ‘Pattern Setups and Their Completions’, in Proceedings of the Fourteenth International Conference on Concept Lattices and Their Applications, CLA 2018, Olomouc, Czech Republic, June 12-14, 2018, D. I. Ignatov and L. Nourine, Eds., in CEUR Workshop Proceedings, vol. 2123. CEUR-WS.org, 2018, pp. 243–253. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-2123/paper20.pdf
[14]
A. Belfodil, A. Belfodil, and M. Kaytoue, ‘Anytime Subgroup Discovery in Numerical Domains with Guarantees’, in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II, M. Berlingerio, F. Bonchi, T. Gärtner, N. Hurley, and G. Ifrim, Eds., in Lecture Notes in Computer Science, vol. 11052. Springer, 2018, pp. 500–516. doi: 10.1007/978-3-030-10928-8_30.
[15]
Q. Labernia, V. Codocedo, C. Robardet, and M. Kaytoue, ‘Mining the Lattice of Binary Classifiers for Identifying Duplicate Labels in Behavioral Data’, in Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part II, S. Benferhat, K. Tabia, and M. Ali, Eds., in Lecture Notes in Computer Science, vol. 10351. Springer, 2017, pp. 12–21. doi: 10.1007/978-3-319-60045-1_2.
[16]
V. Codocedo, G. Bosc, M. Kaytoue, J.-F. Boulicaut, and A. Napoli, ‘A Proposition for Sequence Mining Using Pattern Structures’, in Formal Concept Analysis - 14th International Conference, ICFCA 2017, Rennes, France, June 13-16, 2017, Proceedings, K. Bertet, D. Borchmann, P. Cellier, and S. Ferré, Eds., in Lecture Notes in Computer Science, vol. 10308. Springer, 2017, pp. 106–121. doi: 10.1007/978-3-319-59271-8_7.
[17]
A. Belfodil, S. O. Kuznetsov, C. Robardet, and M. Kaytoue, ‘Mining Convex Polygon Patterns with Formal Concept Analysis’, in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, C. Sierra, Ed., ijcai.org, 2017, pp. 1425–1432. doi: 10.24963/IJCAI.2017/197.
[18]
V. Codocedo, J. Baixeries, M. Kaytoue, and A. Napoli, ‘Characterization of Order-like Dependencies with Formal Concept Analysis’, in Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications, Moscow, Russia, July 18-22, 2016, M. Huchard and S. O. Kuznetsov, Eds., in CEUR Workshop Proceedings, vol. 1624. CEUR-WS.org, 2016, pp. 123–134. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1624/paper10.pdf
[19]
O. Cavadenti, V. Codocedo, J.-F. Boulicaut, and M. Kaytoue, ‘What Did I Do Wrong in My MOBA Game? Mining Patterns Discriminating Deviant Behaviours’, in 2016 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, Montreal, QC, Canada, October 17-19, 2016, IEEE, 2016, pp. 662–671. doi: 10.1109/DSAA.2016.75.
[20]
G. Bosc et al., ‘Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships’, in Discovery Science - 19th International Conference, DS 2016, Bari, Italy, October 19-21, 2016, Proceedings, T. Calders, M. Ceci, and D. Malerba, Eds., in Lecture Notes in Computer Science, vol. 9956. 2016, pp. 19–34. doi: 10.1007/978-3-319-46307-0_2.
[21]
M. Kaytoue, V. Codocedo, A. Buzmakov, J. Baixeries, S. O. Kuznetsov, and A. Napoli, ‘Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing’, in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, A. Bifet, M. May, B. Zadrozny, R. Gavaldà, D. Pedreschi, F. Bonchi, J. S. Cardoso, and M. Spiliopoulou, Eds., in Lecture Notes in Computer Science, vol. 9286. Springer, 2015, pp. 227–231. doi: 10.1007/978-3-319-23461-8_19.
[22]
P. Houdyer, A. Zimmermann, M. Kaytoue, M. Plantevit, J. Mitchell, and C. Robardet, ‘Gazouille: Detecting and Illustrating Local Events from Geolocalized Social Media Streams’, in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, A. Bifet, M. May, B. Zadrozny, R. Gavaldà, D. Pedreschi, F. Bonchi, J. S. Cardoso, and M. Spiliopoulou, Eds., in Lecture Notes in Computer Science, vol. 9286. Springer, 2015, pp. 276–280. doi: 10.1007/978-3-319-23461-8_29.
[23]
O. Cavadenti, V. Codocedo, J.-F. Boulicaut, and M. Kaytoue, ‘When cyberathletes conceal their game: Clustering confusion matrices to identify avatar aliases’, in 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, Campus des Cordeliers, Paris, France, October 19-21, 2015, IEEE, 2015, pp. 1–10. doi: 10.1109/DSAA.2015.7344824.
[24]
G. Nascimento et al., ‘Modeling and Analyzing the Video Game Live-Streaming Community’, in 9th Latin American Web Congress, LA-WEB 2014, Ouro Preto, Minas Gerais, Brazil, 22-24 October, 2014, J. M. Almeida, Á. R. P. Jr, R. Baeza-Yates, and F. Benevenuto, Eds., IEEE Computer Society, 2014, pp. 1–9. doi: 10.1109/LAWEB.2014.9.
[25]
M. Kaytoue, Y. Pitarch, M. Plantevit, and C. Robardet, ‘Triggering patterns of topology changes in dynamic graphs’, in 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, August 17-20, 2014, X. Wu, M. Ester, and G. Xu, Eds., IEEE Computer Society, 2014, pp. 158–165. doi: 10.1109/ASONAM.2014.6921577.
[26]
M. Kaytoue, V. Codocedo, J. Baixeries, and A. Napoli, ‘Three Interrelated FCA Methods for Mining Biclusters of Similar Values on Columns’, in Proceedings of the Eleventh International Conference on Concept Lattices and Their Applications, Košice, Slovakia, October 7-10, 2014, K. Bertet and S. Rudolph, Eds., in CEUR Workshop Proceedings, vol. 1252. CEUR-WS.org, 2014, pp. 243–254. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1252/cla2014\_submission\_31.pdf
[27]
G. Bosc, M. Kaytoue-Uberall, C. Raïssi, J.-F. Boulicaut, and P. Tan, ‘Mining Balanced Sequential Patterns in RTS Games’, in ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014), T. Schaub, G. Friedrich, and B. O’Sullivan, Eds., in Frontiers in Artificial Intelligence and Applications, vol. 263. IOS Press, 2014, pp. 975–976. doi: 10.3233/978-1-61499-419-0-975.
[28]
J. Baixeries, M. Kaytoue, and A. Napoli, ‘Characterization of Database Dependencies with FCA and Pattern Structures’, in Analysis of Images, Social Networks and Texts - Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers, D. I. Ignatov, M. Y. Khachay, A. Panchenko, N. Konstantinova, and R. Yavorskiy, Eds., in Communications in Computer and Information Science, vol. 436. Springer, 2014, pp. 3–14. doi: 10.1007/978-3-319-12580-0_1.
[29]
C. Low-Kam, C. Raïssi, M. Kaytoue, and J. Pei, ‘Mining Statistically Significant Sequential Patterns’, in 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013, H. Xiong, G. Karypis, B. Thuraisingham, D. J. Cook, and X. Wu, Eds., IEEE Computer Society, 2013, pp. 488–497. doi: 10.1109/ICDM.2013.124.
[30]
A. Coulet, F. Domenach, M. Kaytoue, and A. Napoli, ‘Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data’, in Formal Concept Analysis, 11th International Conference, ICFCA 2013, Dresden, Germany, May 21-24, 2013. Proceedings, P. Cellier, F. Distel, and B. Ganter, Eds., in Lecture Notes in Computer Science, vol. 7880. Springer, 2013, pp. 76–91. doi: 10.1007/978-3-642-38317-5_5.
[31]
J. Baixeries, M. Kaytoue, and A. Napoli, ‘Computing Similarity Dependencies with Pattern Structures’, in Proceedings of the Tenth International Conference on Concept Lattices and Their Applications, La Rochelle, France, October 15-18, 2013, M. Ojeda-Aciego and J. Outrata, Eds., in CEUR Workshop Proceedings, vol. 1062. CEUR-WS.org, 2013, pp. 33–44. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1062/paper3.pdf
[32]
M. Kaytoue, A. Silva, L. Cerf, W. M. Jr, and C. Raïssi, ‘Watch me playing, i am a professional: a first study on video game live streaming’, in Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume), A. Mille, F. Gandon, J. Misselis, M. Rabinovich, and S. Staab, Eds., ACM, 2012, pp. 1181–1188. doi: 10.1145/2187980.2188259.
[33]
J. Baixeries, M. Kaytoue, and A. Napoli, ‘Computing Functional Dependencies with Pattern Structures’, in Proceedings of The Ninth International Conference on Concept Lattices and Their Applications, Fuengirola (Málaga), Spain, October 11-14, 2012, L. Szathmary and U. Priss, Eds., in CEUR Workshop Proceedings, vol. 972. CEUR-WS.org, 2012, pp. 175–186. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-972/paper15.pdf
[34]
M. Kaytoue, S. O. Kuznetsov, and A. Napoli, ‘Revisiting Numerical Pattern Mining with Formal Concept Analysis’, in IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, T. Walsh, Ed., IJCAI/AAAI, 2011, pp. 1342–1347. doi: 10.5591/978-1-57735-516-8/IJCAI11-227.
[35]
M. Kaytoue, S. O. Kuznetsov, and A. Napoli, ‘Biclustering Numerical Data in Formal Concept Analysis’, in Formal Concept Analysis - 9th International Conference, ICFCA 2011, Nicosia, Cyprus, May 2-6, 2011. Proceedings, P. Valtchev and R. Jäschke, Eds., in Lecture Notes in Computer Science, vol. 6628. Springer, 2011, pp. 135–150. doi: 10.1007/978-3-642-20514-9_12.
[36]
M. Kaytoue, S. O. Kuznetsov, J. Macko, W. M. Jr, and A. Napoli, ‘Mining Biclusters of Similar Values with Triadic Concept Analysis’, in Proceedings of The Eighth International Conference on Concept Lattices and Their Applications, Nancy, France, October 17-20, 2011, A. Napoli and V. Vychodil, Eds., in CEUR Workshop Proceedings, vol. 959. CEUR-WS.org, 2011, pp. 175–190. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-959/paper12.pdf
[37]
Z. Assaghir, A. Napoli, M. Kaytoue, D. Dubois, and H. Prade, ‘Numerical Information Fusion: Lattice of Answers with Supporting Arguments’, in IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011, IEEE Computer Society, 2011, pp. 621–628. doi: 10.1109/ICTAI.2011.98.
[38]
Z. Assaghir, M. Kaytoue, W. M. Jr, and J. Villerd, ‘Extracting Decision Trees from Interval Pattern Concept Lattices’, in Proceedings of The Eighth International Conference on Concept Lattices and Their Applications, Nancy, France, October 17-20, 2011, A. Napoli and V. Vychodil, Eds., in CEUR Workshop Proceedings, vol. 959. CEUR-WS.org, 2011, pp. 319–332. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-959/paper22.pdf
[39]
P. Agarwal, M. Kaytoue, S. O. Kuznetsov, A. Napoli, and G. Polaillon, ‘Symbolic Galois Lattices with Pattern Structures’, in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings, S. O. Kuznetsov, D. Slezak, D. H. Hepting, and B. G. Mirkin, Eds., in Lecture Notes in Computer Science, vol. 6743. Springer, 2011, pp. 191–198. doi: 10.1007/978-3-642-21881-1_31.
[40]
M. Kaytoue-Uberall, Z. Assaghir, N. Messai, and A. Napoli, ‘Two Complementary Classification Methods for Designing a Concept Lattice from Interval Data’, in Foundations of Information and Knowledge Systems, 6th International Symposium, FoIKS 2010, Sofia, Bulgaria, February 15-19, 2010. Proceedings, S. Link and H. Prade, Eds., in Lecture Notes in Computer Science, vol. 5956. Springer, 2010, pp. 345–362. doi: 10.1007/978-3-642-11829-6_22.
[41]
M. Kaytoue, Z. Assaghir, A. Napoli, and S. O. Kuznetsov, ‘Embedding tolerance relations in formal concept analysis: an application in information fusion’, in Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010, J. X. Huang, N. Koudas, G. J. F. Jones, X. Wu, K. Collins-Thompson, and A. An, Eds., ACM, 2010, pp. 1689–1692. doi: 10.1145/1871437.1871705.
[42]
Z. Assaghir, M. Kaytoue, and H. Prade, ‘A Possibility Theory-Oriented Discussion of Conceptual Pattern Structures’, in Scalable Uncertainty Management - 4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. Proceedings, A. Deshpande and A. Hunter, Eds., in Lecture Notes in Computer Science, vol. 6379. Springer, 2010, pp. 70–83. doi: 10.1007/978-3-642-15951-0_12.
[43]
Z. Assaghir, M. Kaytoue, A. Napoli, and H. Prade, ‘Managing Information Fusion with Formal Concept Analysis’, in Modeling Decisions for Artificial Intelligence - 7th International Conference, MDAI 2010, Perpignan, France, October 27-29, 2010. Proceedings, V. Torra, Y. Narukawa, and M. Daumas, Eds., in Lecture Notes in Computer Science, vol. 6408. Springer, 2010, pp. 104–115. doi: 10.1007/978-3-642-16292-3_12.
[44]
M. Kaytoue-Uberall, S. Duplessis, S. O. Kuznetsov, and A. Napoli, ‘Two FCA-Based Methods for Mining Gene Expression Data’, in Formal Concept Analysis, 7th International Conference, ICFCA 2009, Darmstadt, Germany, May 21-24, 2009, Proceedings, S. Ferré and S. Rudolph, Eds., in Lecture Notes in Computer Science, vol. 5548. Springer, 2009, pp. 251–266. doi: 10.1007/978-3-642-01815-2_19.

Book chapters

[1]
J. Baixeries, V. Codocedo, M. Kaytoue, and A. Napoli, ‘Computing Dependencies Using FCA’, in Complex Data Analytics with Formal Concept Analysis, R. Missaoui, L. Kwuida, and T. Abdessalem, Eds., Springer International Publishing, 2022, pp. 135–150. doi: 10.1007/978-3-030-93278-7_6.
[2]
S. Ferré, M. Huchard, M. Kaytoue, S. O. Kuznetsov, and A. Napoli, ‘Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing’, in A Guided Tour of Artificial Intelligence Research: Volume II: AI Algorithms, P. Marquis, O. Papini, and H. Prade, Eds., Springer, 2020, pp. 411–445. doi: 10.1007/978-3-030-06167-8_13.
[3]
Z. Assaghir, M. Kaytoue-Uberall, A. Napoli, and H. Prade, ‘Organiser la fusion d’informations par l’analyse formelle de concepts’, in Fouille de données complexes. Complexité liée aux données multiples, vol. E-21, G. Cleuziou, M. Lebbah, A. Martin, and B. B. Yaghlane, Eds., in RNTI, vol. E-21. , Hermann-Éditions, 2011, pp. 267–287. Accessed: Feb. 05, 2025. [Online]. Available: http://editions-rnti.fr/?inprocid=1001250

Editorship

[1]
J. V. Haaren, M. Kaytoue, and J. Davis, Eds., Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, MLSA@PKDD/ECML 2016, Riva del Garda, Italy, September 19, 2016, vol. 1842. in CEUR Workshop Proceedings, vol. 1842. CEUR-WS.org, 2017. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1842
[2]
J. Davis, M. Kaytoue, and A. Zimmermann, Eds., Proceedings of the 4th Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, September 18th, 2017, vol. 1971. in CEUR Workshop Proceedings, vol. 1971. CEUR-WS.org, 2017. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1971
[3]
C. V. Glodeanu, M. Kaytoue, and C. Sacarea, Eds., Formal Concept Analysis - 12th International Conference, ICFCA 2014, Cluj-Napoca, Romania, June 10-13, 2014. Proceedings, vol. 8478. in Lecture Notes in Computer Science, vol. 8478. Springer, 2014. doi: 10.1007/978-3-319-07248-7.

National conferences, Worskshops

[1]
Y. Remil, A. Bendimerad, M. Chambard, R. Mathonat, M. Plantevit, and M. Kaytoue, ‘Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets’, in IEEE International Conference on Data Mining, ICDM 2023 - Workshops, Shanghai, China, December 4, 2023, J. Wang, Y. He, T. N. Dinh, C. Grant, M. Qiu, and W. Pedrycz, Eds., IEEE, 2023, pp. 1221–1230. doi: 10.1109/ICDMW60847.2023.00159.
[2]
J. Baixeries, V. Codocedo, M. Kaytoue, and A. Napoli, ‘Dependency Covers from an FCA Perspective’, in Proceedings of the 11th International Workshop ‘What can FCA do for Artificial Intelligence?’ co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R. China; August 20, 2023, S. O. Kuznetsov, A. Napoli, and S. Rudolph, Eds., in CEUR Workshop Proceedings, vol. 3489. CEUR-WS.org, 2023, pp. 57–68. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-3489/paper6.pdf
[3]
Y. Remil, A. Bendimerad, M. Plantevit, C. Robardet, and M. Kaytoue, ‘Découverte de sous-groupes de prédictions interprétables pour le triage d’incidents’, in Extraction et Gestion des Connaissances, EGC 2022, Blois, France, 24 au 28 janvier 2022, S. Amer-Yahia and A. Soulet, Eds., in RNTI, vol. E-38. Editions RNTI, 2022, pp. 411–418. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002754
[4]
J. Baixeries, V. Codocedo, M. Kaytoue, and A. Napoli, ‘Building a Representation Context Based on Attribute Exploration Algorithms’, in Proceedings of the 8th International Workshop ‘What can FCA do for Artificial Intelligence?’ (FCA4AI 2020) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 2020, S. O. Kuznetsov, A. Napoli, and S. Rudolph, Eds., in CEUR Workshop Proceedings, vol. 2729. CEUR-WS.org, 2020, pp. 141–152. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-2729/paper14.pdf
[5]
R. Mathonat, J.-F. Boulicaut, and M. Kaytoue, ‘Découverte de sous-groupes à partir de données séquentielles par échantillonnage et optimisation locale’, in Extraction et Gestion des connaissances, EGC 2019, Metz, France, January 21-25, 2019, M.-C. Rousset and L. Boudjeloud-Assala, Eds., in RNTI, vol. E-35. Éditions RNTI, 2019, pp. 153–164. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002477
[6]
N. Juniarta, V. Codocedo, M. Couceiro, M. Kaytoue, and A. Napoli, ‘Pattern Structures for Identifying Biclusters with Coherent Sign Changes’, in Supplementary Proceedings of ICFCA 2019 Conference and Workshops, Frankfurt, Germany, June 25-28, 2019, D. Cristea, F. L. Ber, R. Missaoui, L. Kwuida, and B. Sertkaya, Eds., in CEUR Workshop Proceedings, vol. 2378. CEUR-WS.org, 2019, pp. 1–13. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-2378/longICFCA1.pdf
[7]
Q. Labernia, V. Codocedo, M. Kaytoue, and C. Robardet, ‘Découverte de labels dupliqués par l’exploration du treillis des classifieurs binaires’, in 16ème Journées Francophones Extraction et Gestion des Connaissances, EGC 2016, 18-22 Janvier 2016, Reims, France, C. de Runz and B. Crémilleux, Eds., in Revue des Nouvelles Technologies de l’Information, vol. E-30. Éditions RNTI, 2016, pp. 255–266. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002177
[8]
V. Codocedo, J. Baixeries, M. Kaytoue, and A. Napoli, ‘Characterization of Order-like Dependencies with Formal Concept Analysis’, in Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications, Moscow, Russia, July 18-22, 2016, M. Huchard and S. O. Kuznetsov, Eds., in CEUR Workshop Proceedings, vol. 1624. CEUR-WS.org, 2016, pp. 123–134. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1624/paper10.pdf
[9]
O. Cavadenti, V. Codocedo, M. Kaytoue, and J.-F. Boulicaut, ‘Découverte de motifs intelligibles et caractéristiques d’anomalies dans les traces unitaires’, in 16ème Journées Francophones Extraction et Gestion des Connaissances, EGC 2016, 18-22 Janvier 2016, Reims, France, C. de Runz and B. Crémilleux, Eds., in Revue des Nouvelles Technologies de l’Information, vol. E-30. Éditions RNTI, 2016, pp. 27–38. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002153
[10]
A. Belfodil, M. Kaytoue, C. Robardet, M. Plantevit, and J. Zarka, ‘Une méthode de découverte de motifs contextualisés dans les traces de mobilité d’une personne’, in 16ème Journées Francophones Extraction et Gestion des Connaissances, EGC 2016, 18-22 Janvier 2016, Reims, France, C. de Runz and B. Crémilleux, Eds., in Revue des Nouvelles Technologies de l’Information, vol. E-30. Éditions RNTI, 2016, pp. 63–68. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002156
[11]
A. Zimmermann, M. Kaytoue, M. Plantevit, C. Robardet, and J.-F. Boulicaut, ‘Profiling Users of the Velo’v Bike Sharing System’, in Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015, I. Katakis, F. Schnitzler, T. Liebig, D. Gunopulos, K. Morik, G. L. Andrienko, and S. Mannor, Eds., in CEUR Workshop Proceedings, vol. 1392. CEUR-WS.org, 2015, pp. 63–64. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1392/paper-08.pdf
[12]
O. Cavadenti, V. Codocedo, M. Kaytoue, and J.-F. Boulicaut, ‘Identifying Avatar Aliases in StarCraft 2’, in Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2015 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 11th, 2015, J. Davis, J. V. Haaren, and A. Zimmermann, Eds., in CEUR Workshop Proceedings, vol. 1970. CEUR-WS.org, 2015, pp. 28–35. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1970/paper-05.pdf
[13]
G. Bosc, M. Kaytoue, M. Plantevit, F. D. Marchi, M. Bensafi, and J.-F. Boulicaut, ‘Vers la découverte de modèles exceptionnels locaux : des règles descriptives liant les molécules à leurs odeurs’, in 15èmes Journées Francophones Extraction et Gestion des Connaissances, EGC 2015, 27-30 Janvier 2015, Luxembourg, B. Otjacques, J. Darmont, and T. Tamisier, Eds., in Revue des Nouvelles Technologies de l’Information, vol. E-28. Hermann-Éditions, 2015, pp. 305–316. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1002091
[14]
G. Bosc, M. Kaytoue-Uberall, C. Raïssi, and J.-F. Boulicaut, ‘Fouille de motifs séquentiels pour l’élicitation de stratégies à partir de traces d’interactions entre agents en compétition’, in 14èmes Journées Francophones Extraction et Gestion des Connaissances, EGC 2014, Rennes, France, 28-32 Janvier, 2014, C. Reynaud, A. Martin, and R. Quiniou, Eds., in Revue des Nouvelles Technologies de l’Information, vol. E-26. Hermann-Éditions, 2014, pp. 359–370. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1001948
[15]
G. Bosc, M. Kaytoue, C. Raïssi, and J.-F. Boulicaut, ‘Strategic Patterns Discovery in RTS-games for E-Sport with Sequential Pattern Mining’, in Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013), Prague, Czech Republic, September 27th, 2013, J. Davis, J. V. Haaren, and A. Zimmermann, Eds., in CEUR Workshop Proceedings, vol. 1969. CEUR-WS.org, 2013, pp. 11–20. Accessed: Feb. 05, 2025. [Online]. Available: https://ceur-ws.org/Vol-1969/paper-03.pdf
[16]
M. Kaytoue, S. O. Kuznetsov, A. Napoli, J. Macko, and W. M. Jr, ‘Caractérisation et extraction de biclusters de valeurs similaires avec l’analyse de concepts triadiques’, in Extraction et gestion des connaissances (EGC’2012), Actes, janvier 31 - février 2012, Bordeaux, France, Y. Lechevallier, G. Melançon, and B. Pinaud, Eds., in Revue des Nouvelles Technologies de l’Information, vol. RNTI-E-23. Hermann-Éditions, 2012, pp. 65–76. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1001166
[17]
M. Kaytoue-Uberall, S. Duplessis, and A. Napoli, ‘L’analyse formelle de concepts pour l’extraction de connaissances dans les données d’expression de gènes’, in Extraction et gestion des connaissances (EGC’2009), Actes, Strasbourg, France, 27 au 30 janvier 2009, J.-G. Ganascia and P. Gançarski, Eds., in Revue des Nouvelles Technologies de l’Information, vol. RNTI-E-15. Cépaduès-Éditions, 2009, pp. 439–440. Accessed: Feb. 05, 2025. [Online]. Available: https://editions-rnti.fr/?inprocid=1000793

Demos

[1]
A. Bendimerad, Y. Remil, R. Mathonat, and M. Kaytoue, ‘On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report’, in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023, S. Chandra, K. Blincoe, and P. Tonella, Eds., ACM, 2023, pp. 1820–1831. doi: 10.1145/3611643.3613876.
[2]
G. Bosc, M. Plantevit, J.-F. Boulicaut, M. Bensafi, and M. Kaytoue, ‘h(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience’, in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part III, B. Berendt, B. Bringmann, É. Fromont, G. C. Garriga, P. Miettinen, N. Tatti, and V. Tresp, Eds., in Lecture Notes in Computer Science, vol. 9853. Springer, 2016, pp. 17–21. doi: 10.1007/978-3-319-46131-1_4.
[3]
P. Houdyer, A. Zimmermann, M. Kaytoue, M. Plantevit, J. Mitchell, and C. Robardet, ‘Gazouille: Detecting and Illustrating Local Events from Geolocalized Social Media Streams’, in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, A. Bifet, M. May, B. Zadrozny, R. Gavaldà, D. Pedreschi, F. Bonchi, J. S. Cardoso, and M. Spiliopoulou, Eds., in Lecture Notes in Computer Science, vol. 9286. Springer, 2015, pp. 276–280. doi: 10.1007/978-3-319-23461-8_29.
[4]
M. Kaytoue, A. Napoli, and L. Szathmary, ‘Une brève présentation du système de fouille de données Coron’, in Extraction et gestion des connaissances (EGC’2012), Actes, janvier 31 - février 2012, Bordeaux, France, Y. Lechevallier, G. Melançon, and B. Pinaud, Eds., in Revue des Nouvelles Technologies de l’Information, vol. RNTI-E-23. Hermann-Éditions, 2012, pp. 603–606.

Software

MinIntChange

An algorithm to mine (frequent) closed numerical patterns and their generators (convex hulls) https://github.com/mehdi-kaytoue/MinIntChange

Sc2Gears4DM

A plugin for SC2Gears that allows to generate various sequences mining algorithms https://github.com/mehdi-kaytoue/Sc2Gears4DM

Trimax

TriMax algorithm for computing maximal biclusters of similar values https://github.com/mehdi-kaytoue/trimax

Cosmic

An algorithm for Mining Contextual Exceptional Subgraphs https://github.com/mehdi-kaytoue/contextual-exceptional-subgraph-mining

Balancespan

Algorithms to mine balanced sequential patterns, jointly realized with Guillaume Bosc during his master studies

Explanations - Code

Coron

Coron is a data-mining suite of software for formal concept analysis and pattern mining created by Lazlso Szathmary. I contributed to the developments and dissemination.

Webpage - Prensation (in French)

Contact

Feel free to reach out via LinkedIn