Ci-dessous le rank A correspond au classement officiel d’ERA, CORE (http://core.edu.au).

Revues internationales avec comité de lecture (Total: 6)
1. Zouinina S., Bennnani Y., Rogovschi N., Lyhyaoui A.: Data Anonymization through  Collaborative Multi-view Microaggregation, Journal of Intelligent Systems, De Gruyter Poland Ltd. 2020.
2. Zouinina S., Rogovschi N., Grozavu N., Matei B., Ozawa S.: t-Distributed Stochastic Neighbor Embedding Spectral Clustering using higher order approximations, in Australian Journal of Intelligent Information Processing Systems (AJIIPS), 2019. (rank A)
3. Salah A., Rogovschi N., Nadif M.: A dynamic collaborative filtering system via a weighted clustering approach. Neurocomputing , 175, Part A: 206 - 215. 2016. (rank A)
4. Rogovschi N., Lebbah M., Bennani Y.: A Self-Organizing Map for Mixed Continuous and Categorical Data, in IJC, International Journal of Computing, ISSN 1727-6209, 2011.
5. Rogovschi N., Lebbah M., Bennani Y.: Learning Self-Organizing Mixture Markov Models. Journal of Nonlinear Systems and Applications (JNSA), ISSN 1918-3704, Published by Watam press, Canada, 2010.
6. Lebbah M., Bennani Y., Rogovschi N. :A Probabilistic Self-Organizing Map for Binary Data Topographic Clustering , International Journal of Computational Intelligence and Applications 7(4): 363-383, 2008.

1.5.2 Revue nationale avec comité de lecture (Total: 1)
1. Rogovschi N., Lebbah M., Bennani Y.: Modèles de mélanges topologiques pour
la classification de données catégorielles et mixtes. Numéro spécial : Apprentissage

Artificiel et Fouille de Données, RNTI 2011-(Revue des Nouvelles Technologies de
l’Information). Editions Hermann
1.5.3 Conférences Internationales avec comité de lecture (Total: 17)
1. Zouinina S., Bennani Y., Rogovschi N. Lyhyaoui, A.: A Two-Levels Data Anonymization
Approach AIAI 2020: Artificial Intelligence Applications and Innovations 85-95.
2. Zouinina S., Grozavu N., Bennani Y., Lyhyaoui, A. Rogovschi N.: A Topological
K-anonymity Model Based on Collaborative Multi-view Clustering. ICANN (3) 2018:
817-827
3. Zouinina S., Grozavu N., Bennani Y., Lyhyaoui, A. Rogovschi N.: Efficient Kanonymization
through Constrained Collaborative Clustering. SSCI 2018: 405-411
4. Grozavu N., Rogovschi N., Bennani Y., Ozawa, S.: Topological co-clustering and
visualization for heterogeneous data, in Proc. XXI International Symposium on Mathematical
Methods Applied to the Sciences (SIMMAC), San José, Costa Rica, 22/02-
02/03 2018, UCR.
5. Benlamine K., Grozavu N., Bennani Y., Rogovschi N., Haddadou K., Amamou A.:
Domain Name Recommendation based on Neural Network. INNS Conference on Big
Data 2018: 60-70
6. Rogovschi N., Kitazono J., Grozavu N., Omori T., Ozawa S.: t-Distributed stochastic
neighbor embedding spectral clustering. IJCNN 2017: 1628-1632
7. Kitazono, J., Grozavu N., Rogovschi N., Omori T., Ozawa S.: t-Distributed Stochastic
Neighbor Embedding with Inhomogeneous Degrees of Freedom. ICONIP (3) 2016:
119-128
8. Salah A., Rogovschi N., Nadif M.: Stochastic Co-clustering for Document-Term
Data . In Proceedings of the SIAM International Conference on Data Mining (SDM’16),
Miami, FL, United States, 2016.
9. Salah A., Rogovschi N., Nadif M.: Model-based Co-clustering for High Dimensional
Sparse Data. In Proceedings of the Nineteenth International Conference on Artificial
Intelligence and Statistics (AISTAT’16), Cadiz, Spain, 2016.
10. Salah A., Rogovschi N., Nadif M.: An Efficient Incremental Collaborative Filtering
System. In Neural Information Processing - 22nd International Conference, ICONIP,
2015, Istanbul, Turkey, November 9-12, 2015, Proceedings, Part III, pages 375–383,
2015.

11. Rogovschi N., Grozavu N., Bennani Y., Ozawa S.: t-Distributed Stochastic Neighbor
Embedding based Self Organizing Maps , in Proc. ISI’17 : 61st International
Statistical Institute World Statistics Congress, 16-21 July, Marrakech, Kingdom of
Morocco.
12. Rogovschi N., Grozavu N., Labiod L.: Spectral Clustering Trough Topological
Learning for Large Datasets. ICONIP (2) 2015: 216-223 (rank A)
13. Grozavu N., Rogovschi N., Cabanes G., Troya-Galvis A., Gancarski P.: VHR satellite
image segmentation based on topological unsupervised learning. MVA 2015: 543-
546
14. Rogovschi N., Grozavu N.: Opinion retrieval through unsupervised topological
learning. IJCNN 2014: 3130-3134 (rank A)
15. Nikulin V., Rogovschi N., Grozavu N.: Incremental Learning from Several Different
Microarrays, in Proc. IJCNN, IEEEInternational Joint Conference on Neural
Network, Dallas, TX- August 4-9, 2013. (rank A)
16. Rogovschi N., Grozavu N.: A Content-based Image Retrieval System Based on Unsupervised
Topological Learning, in Proc. ICMIA’10 : IEEE International Conference
on Data Mining and Intelligent Information Technology Applications, 2010, p.398-394,
Seoul, Korea, 2010
17. Grozavu N., Rogovschi N.: Mining Visual Data, ICMCS09, October 1-4 2009,
Chisinau, Moldavie.
1.5.4 Conférences Nationales avec comité de lecture (Total: 5)
1. Salah A., Rogovschi N., Role F., Nadif M.: Pour une meilleure exploitation de la
classification croisée dans les systèmes de filtrage collaboratif. In EGC 2015, 27-30
Janvier 2015, Luxembourg, pages 347–358, 2015.
2. Rogovschi N., Labiod L., Nadif M.: Un Algorithme Spectral pour le Co-clustering
Topographique. In Extraction et gestion des connaissances (EGC’2012), Actes, janvier
31 - février 2012, Bordeaux, France, pages 579–580, 2012.
3. Rogovschi N., Nadif M. : Classification topologique probabiliste pour des données
catégorielles. In Extraction et gestion des connaissances (EGC’2012), Actes, janvier
31 - février 2012, Bordeaux, France, pages 179–188, 2012.
4. Rogovschi N., Lebbah M., Grozavu N.: Pondération et classification simultanée de
données binaires et continues, in Proc. of the EGC’11, Brest, 25-28 janvier 2011 -
RNTI, Revue des Nouvelles Technologies de l’Information, Editions Hermann.

5. Rogovschi N., Lebbah M., Bennani Y. : Classification non supervisée pondérée de
données mixtes, CAp’10 : Conférence francophone sur l’apprentissage automatique,
17-19 Mai, Clermont-Ferrand, France.
1.5.5 Communications orales (Total: 12)
1. Representation Learning and Data Visualization, séminaire DeepTeams juin 2020.
2. New challenges for dimension reduction techniques, Fès (Morocco), March 2019, Invited
Talk
3. Representation Learning and Topological Co-Clustering , Brasov (Romania), July
2018, Invited Talk
4. Data Anonymization using Topological Unsupervised Learning, Central Washington
University, (USA), Mai 2017, Invited talk
5. Dynamical Collaborative Filtering Systems using weighted clustering, Kobe University,
Kobe (Japon), March 2016, Invited Talk
6. Mixture models for Data Clustering and Visualization, Kobe University, Kobe (Japon),
Mai 2015, Invited Talk
7. Representation Learning and Data Visualization, Vyatka State University, Kirov (Russia),
Mai 2014, Invited Talk
8. Classification non supervisée à base de modèles de mélanges topologiques : Application
à la fouille de données catégorielles, continues et séquentielles, 20 décembre 2012,
LSIS, Univ.Toulon Var. Invited talk. http://seminaire-dyni.univ-tln.fr/
9. A Content-based Image Retrieval System Based on Unsupervised Topological Learning,
Institute of Mathematics and Computer Science A.S.M (Academy of Science of
Moldova), 2011, Chisinau, Moldavie, Invited talk.
10. Apprentissage topologique non-supervisé pour différents types de données, Journées
du LIPADE, 16 juin 2011, France. Présentation.
11. Apprentissage non-supervisé via les modèles de mélanges topologiques, LIPADE, Université
Paris 5, 15 avril 2010, France.
12. Classification à base de modèle de mélanges topologiques, GREYC- ENSICAEN, 22
avril 2010, Caen, France