Publications

Revues internationales avec comité de sélection

• Goffinet, Etienne, Mustapha Lebbah, Azzag, Hanane, Giraldi Loïc, and Anthony Coutant. Functional non-parametric latent block model : A multivariate time series clustering approach for autonomous driving validation. Computational Statistics & Data Analysis, 176(C), 2022. SJR Q1.

•  Forest, Florent, Mustapha Lebbah, Azzag, Hanane, and Jérôme Lacaille. Deep embedded self-organizing maps for joint representation learning and topology-preserving clustering. Neural Computing and Applications, 2021. SJR Q1.

• Mohammed Oualid Attaoui, Azzag, Hanane, Mustapha Lebbah, and Nabil Keskes. Subspace data stream clustering with global and local weighting models. Neural Computing and Applications, 33 :3691–3712, 2021. SJR Q1.

• Mohammed Oualid Attaoui, Nassima Dif, Hanene Azzag, Mustapha Lebbah, and Keskes Nabil. Regions of interest selection in histopathological images using subspace and multi- objective stream clustering. The Visual Computer. International Journal of Computer Graphics, 2022  SJR Q2.

•  Nassima Dif, Attaoui, Mohammed Oualid, Zakaria Elberrichi, Mustapha Lebbah, and Azzag, Hanane. Transfer learning from synthetic labels for histopathological images classification. Applied Intelligence, 2022. SJR Q2.

• Gaël Beck , Tarn Duong, Mustapha Lebbah, Azzag, Hanane, and Christophe Cérin. A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering. J. Parallel Distrib. Comput., 134 :128–139, 2019. SJR Q1.

• Hippolyte Léger, Dominique Bouthinon, Mustapha Lebbah, and Azzag, Hanane. An instance based model for scalable ?-subsumption. International Journal on Artificial Intelligence Tools, 26(7) :1860011, 2018. SJR Q3.

• Beck Gael, Azzag, Hanane, Bougeard Stéphanie, Lebbah Mustapha, and Niang Ndèye. A new micro-batch approach for partial least square clusterwise regression. Procedia Computer Science, 144 :239 – 250, 2018. INNS Conference on Big Data and Deep Learning.

•  Mohammed Ghesmoune, Azzag, Hanane, Salima Benbernou, Mustapha Lebbah, Tarn Duong, and Mourad Ouziri. Big data : From collection to visualization. Machine Learning. Special issue "Discovery Science", pages 1–26, 2017. SJR Q1.

• Tarn Duong, Gael  Beck, Hanene Azzag, Mustapha Lebbah. Nearest neighbour estimators of density derivatives, with application to mean shift clustering. Pattern Recognition Letters. doi =http://dx.doi.org/10.1016/j.patrec.2016.06.021

• Mohammed Ghesmoune, Mustapha Lebbah, and Hanane Azzag. (accepté) state-of-the-art on clustering data stream (invited paper). Big Data Analytics journal, 2016

• Mohammed Ghesmoune, Mustapha Lebbah, and Hanane Azzag. A new growing neural gas for clustering data streams. Neural Networks, Special Issue on Neural Network Learning in Big Data, 2016. Volume 78, June 2016, Pages 36–50.  http://dx.doi.org/10.1016/j.neunet.2016.02.003

• N-Q. Doan, H. Azzag, and M. Lebbah. Growing Self-organizing Trees for Autonomous Hierarchical Clustering,  Neural Networks. Special Issue on Autonomous Learning (2012). Elsevier.

• Amine Chaibi, Mustapha Lebbah and Hanane Azzag. Group Outlier Factor : a new score using Self-Organising Map for Group-Outlier and Novelty Detection, International Journal of Computational Intelligence and Applications (IJCIA), World Scientific Publishing Company, 2013.

• H. Azzag,  Christiane Guinot,  and G. Venturini. An artificial ants model for fast construction and approximation of proximity graph. Adaptive Behavior December 2012 20: 443-459, first published on August 31, 2012 doi:10.1177/1059712312457186

• Azzag, H.,  Lebbah, M. (2011). Self-Organizing Tree Using Artificial Ants. Journal of Information Technology Research (JITR), 4(2), 1-16. doi:10.4018/jitr.2011040101.

• H. Azzag, G. Venturini, A. Oliver, C. Guinot. A hierarchical ant based clustering algorithm and its use in three real-world applications. Special Issue on Applications of Metaheuristics, European Journal of Operational Research. Wout Dullaert, Marc Sevaux, Kenneth Sörensen and Johan Springael Editors, Volume 179, Issue 3, page 906-922, 2007.

•  F. Picarougne, H. Azzag, G. Venturini, C. Guinot. - A New Approach of Data Clustering Using a Flock of Agents. Picarougne F., Azzag H., Venturini G., and Guinot C. 2007. Evol. Comput. 15, 3 (Sep. 2007), pages 345-367

Chapitre de livre

• Leila Abidi, Hanane Azzag, Salima Benbernou, Mehdi Bentounsi, Christophe Cérin, Tarn Duong, Philippe Garteiser, Mustapha Lebbah, Mourad Ouziri, Soror Sahri, and Michel Smadja, editors. A Big Data Platform for Enhancing Life Imaging Activities. Utilizing Big Data Paradigms for Business Intelligence. IGI Global, 2018. 

• Cristinel Diaconu, S. Kraml, Christian Surace, Daniel Chateigner, Thérèse Libourel Rouge, Anne Laurent, Yuan Lin, Marc Schaming, Salima Benbernou, Mustapha Lebbah, Danièle Boucon, Christophe Cérin, Azzag, Hanane, Philippe Mouron, Jean-Yves Nief, Stephane Coutin, and Volker Beckmann. PREDON Scientific Data Preservation 2014. Research report, LIRMM, 2014. LPSC14037. 

• Christophe Cérin, Mustapha Lebbah, and Hanane Azzag. Cloud and grid methodologies for data management and preservation. In Cristinel Diaconu (to appear), editor, Scientific data preservation, pages 49–54. 2014 (dans le cadre du projet PREDON (CNRS Mastodons) https://martwiki.in2p3.fr//PREDON) 

• Hanane Azzag, Mustapha Lebbah: A New Way for Hierarchical and Topological Clustering. Advances in Knowledge Discovery and Management 2013 Vol 3: 85-97

• J. Lavergne, H. Azzag, C. Guinot, G. Venturini, On building and visualizing proximity graphs for large data sets with artificial ants. B. Fichet et al. (eds.), Classification and Multivariate Analysis for Complex Data Structures, Studies in Classification, Data Analysis, and Knowledge Organization -Verlag Berlin Heidelberg appear in 2011.

• H. Azzag, C. Guinot, G. Venturini. Data and text mining with hierarchical clustering ants. Swarm intelligence and data mining. Springer SCI series. A. Abraham, C. Grosan and V. Ramos Editors, pages 153-190.

• H. Azzag, F. Picarougne, C. Guinot, G. Venturini. VRMiner: a tool for multimedia databases mining with virtual reality. Processing and Managing Complex Data for Decision Support. J. Darmont and O. Boussaid, Editors, p 318-339.

Revues nationales avec comité de sélection

• J.  Lavergne, H. Azzag, C. Guinot and G. Venturini. Méthode visuelle et interactive de partitionnement d'un ensemble de données à l'aide de graphes de voisinage construits par des fourmis artificielles. Revue des Nouvelles Technologies de l'Information (RNTI), numéro spécial Apprentissage et visualisation. Editions Hermann. Decembre 2010.

• H. Azzag, D. Da Costa, C. Guinot, G. Venturini. Un aperçu de la fouille visuelle de données, accepté, à paraître dans la revue RNTI, numéro spécial sur les journées AAFD-2006 (Apprentissage Artificiel et Fouille de données), 12 pages, 2007. (Revue des Nouvelles Technologies de l'Information),

• J. Lavergne, H. Azzag, C. Guinot, G. Venturini. Construction incrémentale et visualisation de graphes de voisinage par des fourmis artificielles. Extraction et Gestion des Connaissances (EGC 2007), Namur. RNTI-E-9 Cépaduès-Éditions 2007, Volume I, p 135-146.

• F. Mokadem, F. Picarougne, H. Azzag, C. Guinot, G. Venturini. Techniques visuelles de recherche d’informations sur le Web, Revue des Nouvelles Technologies de l'Information (RNTI), numéro spécial Visualisation en Extraction des Connaissances, Pascale Kuntz et François Poulet rédacteurs invités, Cépaduès édition, pages 21-47.

• H. Azzag, F. Picarougne, C. Guinot, G. Venturini. Un survol des algorithmes biomimétiques pour la classification. Revue des Nouvelles Technologies de l'Information (RNTI-C-1), Classification et fouille de données. 2004. P. 13-24, Cépaduès édition.

• H. Azzag, N. Monmarché, M. Slimane, C. Guinot, G. Venturini. Algorithme AntTree : Classification non supervisée par des fourmis artificielles. Revue des Nouvelles Technologies de l'Information (RNTI). Entreposage et fouille de données. 2003. P. 75-86. Cépaduès édition.

Conférences internationales avec comité de sélection

• (Accepted) Reda Khoufache, Mustapha Lebbah and Hanene Azzag, Etienne Goffinet and Djamel Bouchaffra. Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning. SIAM International Conference on Data Mining (SDM 2024). 18-20 April, 2024, Houston, US. arxiv.

• (Accepted) Reda Khoufache, Anis Belhadj, Hanane Azzag, Mustapha Lebbah. Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024). Taipei, Taiwan. 7-10 May 2024. arxiv.

• Alex Mourer, Florent Forest, Mustapha Lebbah, Hanane Azzag, LACAILLE Jerome. Selecting the Number of Clusters K with a Stability Trade-off: an Internal Validation Criterion. Osaka, Japan, from May 25 to May 28, PAKDD 2023. 

• Kodjo Mawuena Amekoe, Mohamed Djallel Dilmi, Hanene Azzag, Zaineb Chelly Dagdia, Mustapha Lebbah et Gregoire Jaffre. « TabSRA : An Attention based Self-Explainable Model for Tabular Learning ». In : The31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2023.

• Mohammed Oualid Attaoui, Hanene Azzag, Mustapha Lebbah. Improved Multi-Objective Data Stream Clustering with Time and Memory Optimization. In International Conference on Genetic and Evolutionary Computing. GECCO 2023. Lisbon, July 15-19, 2023.

• Wyctor Fogos da Rocha, Hanene Azzag, Mustapha Lebbah, Anissa Mokraoui. 2022 21st IEEE International Conference on Machine Learning and Applications. Super-Resolution GAN Improving YOLO's Performance Benchmark

• Mohammed Oualid Attaoui, Hanane Azzag, Nabil Keskes, and Mustapha Lebbah. A new subspace multi-objective approach for the clustering and selection of regions of interests in his19 topathological images. In IEEE Congress on Evolutionary Computation, CEC 2021, Kraków, Poland, June 28 - July 1, 2021, pages 556–563. IEEE, 2021.

•  Étienne Goffinet, Mustapha Lebbah, Hanane Azzag, Loïc Giraldi, and Anthony Coutant. Multivariate time series multi-coclustering. application to advanced driving assistance system validation. In The 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 463–468, 2021.

• Beck, Gael, Mustapha Lebbah, Azzag, Hanane, and Tarn Duong. A new nearest neighbor median shift clustering for binary data. In Igor Farkaš, Paolo Masulli, Sebastian Otte, and Stefan Wermter, editors, Artificial Neural Networks and Machine Learning – ICANN 2021, pages 101–112, Cham, 2021. Springer International Publishing

• Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui, and Hanane Azzag. Experience feedback using representation learning for few-shot object detection on aerial images. In IEEE International Conference on Machine Learning and Applications, 2021, 2021. 

• Étienne Goffinet , Mustapha Lebbah, Hanane Azzag, and Loïc Giraldi. Autonomous driving validation with model-based dictionary clustering. In Yuxiao Dong, Dunja Mladenic, and Craig Saunders, editors, Machine Learning and Knowledge Discovery in Databases : Applied Data Science Track - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14- 18, 2020, Proceedings, Part IV, volume 12460 of Lecture Notes in Computer Science, pages 323–338. Springer, 2020. 

•  Forest, Florent, Quentin Cochard, Cecile Noyer, Adrien Cabut, Marc Joncour, Jérôme Lacaille, Mustapha Lebbah, and Azzag, Hanane. Large-scale Vibration Monitoring of Aircraft Engines from Operational Data using Self-organized Models. In Annual Conference of the PHM Society, 2020. 

• Forest, F., Mourer, A., Lebbah, M., Azzag, H., and Lacaille,J. (2021). An Invariance-guided Stability Criterion for Time Series Clustering Validation. In International Conference on Pattern Recognition (ICPR).

•  Mohammed Oualid Attaoui, Mustapha Lebbah, Hanane Azzag, and Nabil Keskes. Multiobjective data stream clustering. In International Conference on Genetic and Evolutionary Computing, GECCO. Springer, 2020. (poster). 

• Mohammed Oualid Attaoui, Mustapha Lebbah, Nabil Keskes, Hanane Azzag, and Mohammed Ghesmoune. Soft subspace growing neural gas for data stream clustering. In Artificial Neural Networks and Machine Learning - ICANN 2019 : Text and Time Series - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part IV, pages 569–580, 2019. 

• Florent Forest, Mustapha Lebbah, Hanane Azzag, and Jérôme Lacaille. Deep embedded som : joint representation learning and self-organization. In The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium from 24 to 26 April, ESANN, pages 1–6, 2019. 

• Florent Forest, Jérôme Lacaille, Mustapha Lebbah, and Hanane Azzag. A generic and scalable pipeline for large-scale analytics of continuous aircraft engine data. In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, pages 1918–1924, 2018.

•  Mohammed Ghesmoune, Mustapha Lebbah, Hanane Azzag, Salima Benbernou, Mourad Ouziri, and Tarn Duong. A complete data science work-flow for insurance field. In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, pages 1925–1930, 2018.

•  Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Hanane Azzag, and Mustapha Lebbah. A distributed rough set theory algorithm based on locality sensitive hashing for an efficient big data pre-processing. In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, pages 2597–2606, 2018. 20

• Nhat-Quang Doan , Hanane Azzag, and Mustapha Lebbah. Hierarchical laplacian score for unsupervised feature selection. In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–7, 2018.

• Beck Gaël andHanane Azzag, Stephanie Bougeard, Ndèye Niang, and Mustapha Lebbah. A new micro-batch approach for partial least square clusterwise regression. In The 3rd INNS Conference on Big Data and Deep Learning (INNS BDDL), April 17-19, 2018, Sanur, Bali, Indonesia, 2018. 

• Hippolyte Léger, Dominique Bouthinon, Mustapha Lebbah, and Hanane Azzag. An instance based model for scalable theta -subsumption. In 29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017, Boston, MA, USA, November 6-8, 2017, pages 846– 852, 2017 

• Hippolyte Leger, Dominique Bouthinon, Mustapha Lebbah, Hanene Azzag, A new Model for Scalable θ-sumbsumption. International Conference on Inductive Logic Programming, ILP 2016. 4th - 6th September 2016.

•  Gael Beck, Tarn Duong, Hanene Azzag and Mustapha Lebbah. Distributed Mean Shift Clustering with Approximate Nearest Neighbours. IEEE WCCI 2016.

• Nesrine Masmoudi, Hanane Azzag, Mustapha Lebbah, Cyrille Bertelle, Maher Ben Jemaa. CL-AntInc Algorithm for Clustering Binary Data Streams Using the Ants Behavior. KES2016. 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

• Mohammed Ghesmoune, Mustapha Lebbah, Hanene Azzag. Micro-Batching Growing Neural Gas for Clustering Data Streams using Spark Streaming. Procedia Computer Science journal  (2015) pp. 158-166. Doi 10.1016/j.procs.2015.07.290. (Paper presented at  INNS Conference on Big Data, 8-10 August 2015 – San Francisco, USA.

• Nhat-Quang Doan, Mohammed Ghesmoune, Hanane Azzag, and Mustapha Lebbah - Growing Hierarchical Trees for Data Stream Clustering and Visualization - International Joint Conference on Neural Networks (IJCNN 2015), July 12–17, 2015, Killarney , Ireland.

• Nesrine Masmoudi, Hanane Azzag, Mustapha Lebbah, Cyrille Bertelle and Maher Ben Jemaa.  How to Use Ants for Data Stream Clustering. IEEE Congress on Evolutionary Computation (IEEE CEC 2015)

• Mohammed Ghesmoune, Mustapha Lebbah, and Hanene Azzag. (accepted) clustering over data streams based on growing neural gas. In The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), PAKDD 2015.

• Mohammed Ghesmoune, Hanene Azzag, and Mustapha Lebbah. G-stream : Growing neural gas over data stream. In Neural Information Processing - 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part I, volume 8834 of Lecture Notes in Computer Science, pages 207–214. Springer, 2014.

• Tugdual Sarazin, Mustapha Lebbah, and Hanane Azzag. Biclustering using spark- mapreduce. In 2014 IEEE International Conference on Big Data, Big Data 2014, Wa- shington, DC, USA, October 27-30, 2014, pages 58–60, 2014.

• Tugdual Sarazin, Mustapha Lebbah, Hanane Azzag, and Amine Chaibi. Feature group weighting and topological biclustering. In Neural Information Processing - 21st Interna- tional Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part II, volume 8835 of Lecture Notes in Computer Science, pages 369–376. Springer, 2014.

• Masmoudi, N.; Azzag, H.; Lebbah, M.; Bertelle, C., "Incremental clustering of data stream using real ants behavior," Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on , vol., no., pp.262,268, July 30 2014-Aug. 1 2014. doi: 10.1109/NaBIC.2014.6921889.http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6921889&isnumber=6921848

• Tugdual Sarazin, Hanane Azzag, and Mustapha Lebbah. SOM clustering using Spark-MapReduce. In Proceedings of 28th IEEE International Parallel & Distributed Processing Symposium - International Workshop on High Performance Data Intensive Computing (IEEE IPDPS HPDIC2014). IEEE Computer Society, 2014

• Nesrine Masmoudi, Hanane Azzag, Mustapha Lebbah and Cyrille Bertelle. Clustering using chemical and colonial odors of real ants. The Fifth World Congress on Nature and Biologically Inspired Computing (NaBIC2013) to be held in Fargo, USA  August 12-14, 2013.

• Amine Chaibi, Mustapha Lebbah and Hanane Azzag, (accepté) A new visualization of group-outliers in unsupervised learning, 17th International Conference Information Visualisation. 15 - 18 July 2013, London, UK.

•  Nhat-Quang Doan, Hanane Azzag, Mustapha Lebbah and Guillaume Santini. Self-organizing Trees for visualizing protein dataset. International Joint Conference on Neural Networks (IJCNN 2013). August 4–9, 2013 Dallas

• Amine Chaibi, Mustapha Lebbah and Hanane Azzag. A New Bi-clustering Approach Using Topological Maps. International Joint Conference on Neural Networks (IJCNN 2013). August 4–9, 2013 Dallas

• Amine Chaibi, Mustapha Lebbah and Hanane Azzag. Novelty Detection using a New Group Outlier Factor. 19th International Conference on Neural Information Processing (ICONIP 2012). Part III, LNCS 7665, pp. 364–372. November 12-15 2012, Doha Qatar. 

• Nhat-Quang Doan, Hanane Azzag, and Mustapha Lebbah. Self-organizing map and tree topology for graph summarization. In Proceedings of the 22st International Conference on Artificial Neural Networks (ICANN 2012), Lecture Notes in Computer Science, Springer, 2012.  Part II, LNCS 7553, pp. 363–370.  September 11-14th, Lausanne, Switzerland.

• Nhat-Quang Doan and Hanane Azzag and Mustapha Lebbah. Graph Decomposition using Self-Organizing Trees. 16th International Conference on Information  Visualisation IV 2012, Montpellier, France, July 2012. pp 246-251. IEEE Computer Society 2012.

• Nhat-Quang Doan, Hanane Azzag and Mustapha Lebbah. Growing Self-organizing Trees for Knowledge Discovery from Data. IEEE World Congress on Computational Intelligence (IEEE WCCI 2012). International Joint Conference on Neural Networks (IJCNN 2012). pages 251 - 258. June 10-15, 2012. Brisbane. Australia

• Amine Chaibi, Hanane Azzag and Mustapha Lebbah. Automatic Group-Outlier Detection. ESANN'2012 - proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pages 393-398. Bruges, Belgium, 25 - 27 April 2012

• Boudjeloud-Assala L., H.azzag. A Cooperative Biomimetic Approach for High Dimensional Data Mining. Genetic and Evolutionary Computation Conference. GECCO 2011.

• M. Lebbah, H. Azzag. Topological Hierarchical Tree Using Artificial Ants.  In Proc Springer LNCS (Lecture Notes in Computer Science) of ICONIP’10 : 17th International Conference on Neural Information Processing. Pages 652-659. 22nd – 25th November 2010 in Sydney, Australia.

• H. Azzag, M. Lebbah, Aymen Arfaoui. Map-TreeMaps : A new approach for hierarchical and topological clustering. Machine Learning and Applications. IEEE-ICMLA 2010: The Ninth International Conference. Washington DC, USA,  December 12-14, 2010.

• Azzag H., Lebbah M. "SoTree : Self-organizing of hierarchical clustering". The 5th International Conference on Neural Network and Artificial Intelligence (ICNNAI'2010), P. 42-46. 1 - 4 June, 2010, State Technical University, Brest, Belarus

• H.Azzag,  M. Lebbah  « A new approach for auto-organizing a groups of artificial ants », in Proc Springer LNCS (Lecture Notes in Computer Science) of the ECAL'2009, 10th European Conference on Artificial Life.  Budapest septembre 13-16, 2009. LNCS/LNAI 5778.

• H. Azzag , M. Lebbah, J. lavergne, N. Rogovsci. Self-Organizing map and Points of Interest. Proceedings of the ICMCS'09, October, 2009, Chisinau, Moldavie.

•  H. Azzag, M. Lebbah Clustering of Self-Organizing Map. ESANN'2008 proceedings - European Symposium on Artificial Neural Networks. Page 209-214. Bruges (Belgium), 23-25 April 2008.

•  J. Lavergne, H. Azzag, C. Guinot, G. Venturini , On Building and Visualizing Proximity Graphs for Large Data Sets with Artificial Ants, the 1st joint meeting of the Société Francophone de Classification and the Classification And Data Analysis Group of SIS (SFC-CLADAG’2008), June 11-13, 2008, Caserta, Italy, pages 349-352.

•  J. Lavergne, H. Azzag, C. Guinot, G. Venturini. Incremental Construction of Neighborhood Graphs using the Ants Self-Assembly Behavior, the 19th annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI'2007), p. 399-406, Vo1.1 , ISSN 1082-3409 , October 29 - 31 , Patras , Greece . 

• H. Azzag, Y. Bennani, M. Lebbah. «A Stochastic algorithm for clustering inspired from cockroaches behavior», Proceedings of the ICMCS'07, September 19-21, 2007, Chisinau, Moldavie. 

• H. Azzag, C. Guinot, G. Venturini. On data clustering with bio-inspired algorithms, Sixth Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, September 2007. 

•  J. Lavergne, H. Azzag, C. Guinot, G. Venturini. On building graphs of documents with artificial ants. The 16th International World Wide Web Conference, (WWW2007). Banff, Canada. Mai, 2007. 

• H. Azzag, D. Ratsimba, D. Da Costa, C. Guinot, G. Venturini. On building maps of web pages with a cellular automata. IFIP Conference on Biologically Inspired Cooperative Computing, WCC-BICC      2006. August 20-25, Santiago, Chile. 

• H. Azzag, D. Ratsimba, D. Da Costa, C. Guinot, G. Venturini. Generating maps of web pages using cellular automata. World Wide Web Conference, (W3C 2006). Edinburgh. 

• H. Azzag, G.  Venturini, C. Guinot. Automatic Generation of Web Portals Using Artificial Ants. Fourteenth International World Wide Web Conference, (W3C 2005). May 10-14, Chiba, Japan.

• H. Azzag, C. Guinot, G.  Venturini. AntTree: web document clustering using artificial ants. Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), P. 480-484, IOS Press. 22- 27 août. Valence. Espagne.

• H. Azzag, C. Guinot, G. Venturini. How to use ants for hierarchical clustering. Fourth international workshop on Ant Colony Optimization and Swarm Intelligence, Ants 2004.  P.350-357, LNCS 3172, 5-8 septembre. Brussels, Belgique.

• F. Picarougne, H. Azzag, C.  Guinot, G. Venturini. On data clustering with a flock of artificial agents. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 04), 2004, P.777-778, Boca Raton, Florida, USA.

• H. Azzag, N. Monmarché, M. Slimane, C. Guinot, G. Venturini. AntTree: a New Model for Clustering with Artificial Ants. The IEEE Congress on Evolutionary Computation, CEC 2003. P. 2642-2647. 08-12 décembre. Canberra. Australie.

• H. Azzag, N. Monmarché, M. Slimane, C. Guinot, G. Venturini. A clustering algorithm based on the ants self-assembly behaviour. ECAL 2003. P. 564-571. W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler, editors, Advances in Artificial Life - Proceedings of the 7th European Conference on Artificial Life (ECAL), Lecture Notes in Artificial Intelligence, Vol. 2801, Springer Verlag, 14-17 septembre. Dortmund. Allemagne.

Conférences nationales avec comité de sélection

• Bilal Faye, Hanane Azzag, Mustapha Lebbah, and Fangchen Feng. Normalisation contextuelle : Une nouvelle approche pour la stabilité et l’amélioration des performances des réseaux de neurones. In 24ème Conférences Francophones Extraction et Gestion des Connaissances, EGC, RNTI, page to appear, 2024.

• Reda Khoufache, Mohamed Djallel Dilmi, Hanane Azzag, Étienne Goffinet, and Mustapha Lebbah. Propriétés émergentes du multi-clustering bayésien non paramétrique : Application aux données images multivues. In Société Francophone de Classification (SFC), 2023.

• Reda Khoufache, Mohamed Djallel Dilmi, Hanane Azzag, Étienne Goffinet, and Mustapha Lebbah. Propriétés émergentes du multi-clustering bayésien non paramétrique : Application aux données d’images multivues. In 23ème Conférences Francophones Extraction et Gestion des Connaissances, EGC, RNTI, pages 639–640, 2023.

• Étienne Goffinet, Mustapha Lebbah, Hanane Azzag, Loïc Giraldi, and Anthony Coutant. Multiple co-clustering de séries temporelles. In SFdS 2021 : 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), 2021.

• Forest, Florent, Mustapha Lebbah, Azzag, Hanane, and Jérôme Lacaille. Carte SOM profonde : Apprentissage joint de représentations et auto-organisation. In CAp2020 : Conférence d’Apprentissage, 2020.

• Étienne Goffinet, Mustapha Lebbah, Hanane Azzag, and Loïc Giraldi. Clustering de séries temporelles par construction de dictionnaire. In 20ème Journées Francophones Extraction et Gestion des Connaissances, EGC, volume E-36 of RNTI, pages 181–192, 2020.

• Mohammed Ghesmoune, Mustapha Lebbah, Hanane Azzag. Clustering topologique pour le flux de données. In EGC 2015, vol. RNTI-E-28, pp.137-142

• Tugdual Sarazin, Hanane Azzag, Mustapha Lebbah. Modèle de Biclustering dans un paradigme "Mapreduce". In EGC 2015, vol. RNTI-E-28, pp.467-468

• Amine Chaibi, Hanane Azzag, and Mustapha Lebbah. Pondération de blocs de variables en bi-partitionnement topologique. In Extraction et Gestion des Connaissances (EGC’2014), Revue des Nouvelles Technologies de l’Information. Hermann, 2014. RNTI-E-26, pp.317-328 

• A. Chaibi, M. Lebbah and H. Azzag. Nouvelle approche de bi-partitionnement topologique. In Proc. of the EGC'13. 29 janvier - 01 février 2013, Toulouse, France – RNTI, Revue des Nouvelles Technologies de l'Information, Editions Hermann

•  Nhat-Quang Doan, Hanane Azzag and Mustapha Lebbah. Sélection de variables non supervisée sous contraintes hiérarchiques. In Proc. of the EGC'13. 29 janvier - 01 février 2013, Toulouse, France – RNTI, Revue des Nouvelles Technologies de l'Information, Editions Hermann

• Amine Chaibi, Mustapha Lebbah and Hanane Azzag. Détection de groupes outliers en classification non supervisée. in Proc. of the EGC'12,  pages 119-125. 31 janvier - 3 février, Bordeaux, France – RNTI, Revue des Nouvelles Technologies de l'Information, Editions Hermann. 

• Nhat-Quang Doan, Hanane Azzag and Mustapha Lebbah. Clustering multi-niveaux de graphes : hiérarchique et topologique. In Proc. of the EGC'12, pages 567-569. 31 janvier - 3 février, Bordeaux, France – RNTI, Revue des Nouvelles Technologies de l'Information, Editions Hermann. (Poster)

• Azzag H., Lebbah M.. (2011),  «Une nouvelle approche visuelle pour la classification hiérarchique et topologique», EGC'11, Brest, 25-28 janvier 2011 – RNTI, Revue des Nouvelles Technologies de l'Information, Editions Hermann.

• H. Azzag, M.Lebbah. Auto-organisation topologique et hiérarchique des données. Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances. EGC’10. Pages 555-560 (Revue des Nouvelles Technologies de l'information).

• H. Azzag, M. Lebbah. CLassification topologique et points d’intérêt, XVIIe Rencontre de la Société Francophone de Classification (SFC'2010),  Juin 2010. P37-40

• L. Boudjeloud-Assala,  H.Azzag. Sélection d’espace optimal pour les graphes multidimensionnels. XVIIe Rencontre de la Société Francophone de Classification (SFC'2010),  Juin 2010. P107-110

• L. Boudjeloud-Assala, H.Azzag. Graphes multidimensionnels : Approche Coopératives.  Atelier “Fouille de Données Complexes dans un processus d'extraction de connaissances” au sein de la Conférence Extraction et Gestion des Connaissances (EGC'2010), 27 janvier, Hammamet, 2010.

• L. Boudjeloud-Assala, H.Azzag. Approche biomimétique coopérative pour la visualisation de grands graphes multidimensionnels. Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances. EGC’10.  (Revue des Nouvelles Technologies de l'information) Pages 667-668 (poster).

• M. Lebbah, H. Azzag .Segmentation hiérarchique des cartes topologiques. 8ème journées francophones : Extraction et gestion des Connaissances, EGC’08, Nice, Janvier 2008.  Pages 631-642 (Revue des Nouvelles Technologies de l'Information).

• J. Lavergne, H. Azzag, C. Guinot, G. Venturini (2009). Découverte visuelle et interactive d'un partitionnement de données à l'aide de graphes de voisinage construits par des fourmis artificielles, XIe Conférence francophone sur l’Apprentissage Artificiel (CAP‘2009), pp 225-236.

• J. Lavergne, H. Azzag, C. Guinot, G. Venturini (2009). Classification visuelle interactive à l'aide de graphes de voisinage construits par des fourmis artificielles, Atelier “ Topological learning” au sein de la Conférence Extraction et Gestion des Connaissances (EGC'2009), 12 pages, France.

• J. Lavergne, H. Azzag, C. Guinot, G. Venturini (2009). Construction d'un graphe hypertexte de documents par des fourmis artificielles, Xe Conférence Hypertexte et hypermédia Produits, Outils et Méthodes (H2PTM‘2009), p 139-150.

• J. Lavergne, H. Azzag, C. Guinot et G. Venturini. Une approche incrémentale d’une méthode de classification non supervisée par nuages d’insectes volants, XIVe Rencontre de la Société Francophone de Classification (SFC'2007), p 117-120, 2007.

• L. Chalet, H. Azzag, C. Guinot, G. Venturini. Un algorithme préliminaire pour la structuration visuelle de données inspiré des modèles de construction de nids chez les insectes sociaux. Actes de l’atelier « Fouille de données et Algorithmes Biomimétiques » lors de la conférence EGC’2007 (Extraction et Gestion des Connaissances, Namur, Belgique), p 13-24.

• H. Azzag, C. Guinot, G. Venturini. Classification hiérarchique et visualisation de pages Web. 6ème journées francophones Extraction et Gestion des Connaissances (EGC 2006). Atelier Fouille du web, pages 5-16.

• H. Azzag, D. Ratsimba, A. Alarabi, C. Guinot, G. Venturini. Cartes de pages Web obtenues par un automate cellulaire. 6ème journées francophones Extraction et Gestion des Connaissances (EGC 2006). Atelier Fouille du web, pages 17-28.

• H. Azzag, F. Picarougne, C. Guinot, G. Venturini. Classification de données par automate cellulaire. 12ème Rencontres de la Société Francophone de Classification (SFC2005). P. 47-50, 30-01 juin. Montréal, Canada.

• F. Mokaddem, F. Picarougne, H. Azzag, C. Guinot, G. Venturini. EClaViSeR : Classification visuelle et interactive pour la recherche d'informations sur le Web. 12ème Rencontres de la Société Francophone de Classification (SFC2005). P. 211-214, 30-01 juin. Montréal, Canada.

• H. Azzag, C. Guinot, G. Venturini. Classification automatique de documents : application au web. 11ème Rencontres de la Société Francophone de Classification (SFC 2004). P. 91-94. 8-10 septembre. Bordeaux. France.

• H. Azzag, C. Guinot, G.  Venturini. Algorithme biomimétique et classification. 11ème Rencontres de la Société francophone de Classification (SFC 2004). P. 11-18 .8-10 septembre. Bordeaux. France.

• H. Azzag, N. Monmarché, M. Slimane, C. Guinot, G. Venturini. Algorithme AntTree : Classification non supervisée par des fourmis artificielles. P.137-140. XXXV journées de statistique, (SFDS 2003). Lyon. France.

• H. Azzag, N. Monmarché, C. Guinot, M. Slimane, G. Venturini. Classification arborescente de données par auto-assemblage de fourmis artificielles. 10ème Rencontres de la Société Francophone de Classification (SFC 2003). Presses académiques de neuchâtel. P. 55-58. 09-12 septembre. Neuchâtel. Suisse.