State-of-the-art on clustering data streams M Ghesmoune, M Lebbah, H Azzag Big Data Analytics 1, 1-27, 2016 | 90 | 2016 |
A new growing neural gas for clustering data streams M Ghesmoune, M Lebbah, H Azzag Neural Networks 78, 36-50, 2016 | 53 | 2016 |
An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment M Sulaiman, Z Halim, M Lebbah, M Waqas, S Tu Journal of Grid Computing 19, 1-31, 2021 | 47 | 2021 |
Biophysical regions identification using an artificial neuronal network: A case study in the South Western Atlantic M Saraceno, C Provost, M Lebbah Advances in Space Research 37 (4), 793-805, 2006 | 42 | 2006 |
From variable weighting to cluster characterization in topographic unsupervised learning N Grozavu, Y Bennani, M Lebbah | 41 | 2009 |
SOM clustering using spark-mapreduce T Sarazin, H Azzag, M Lebbah 2014 IEEE International Parallel & Distributed Processing Symposium …, 2014 | 39 | 2014 |
BeSOM: Bernoulli on self-organizing map M Lebbah, N Rogovschi, Y Bennani 2007 International joint conference on neural networks, 631-636, 2007 | 35 | 2007 |
S. Topological Map for Binary Data, ESANN 2000, Bruges M Lebbah, F Badran, F Thiria April 26-27-28, 2000, Proceedings, 2000 | 31* | 2000 |
A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering G Beck, T Duong, M Lebbah, H Azzag, C Cérin Journal of Parallel and Distributed Computing 134, 128-139, 2019 | 30 | 2019 |
Transfer learning from synthetic labels for histopathological images classification N Dif, MO Attaoui, Z Elberrichi, M Lebbah, H Azzag Applied Intelligence 52 (1), 358-377, 2022 | 28 | 2022 |
Nearest neighbour estimators of density derivatives, with application to mean shift clustering T Duong, G Beck, H Azzag, M Lebbah Pattern Recognition Letters 80, 224-230, 2016 | 27 | 2016 |
Micro-batching growing neural gas for clustering data streams using spark streaming M Ghesmoune, M Lebbah, H Azzag Procedia Computer Science 53, 158-166, 2015 | 26 | 2015 |
Feature selection for self-organizing map K Benabdeslem, M Lebbah 2007 29th International Conference on Information Technology Interfaces, 45-50, 2007 | 25 | 2007 |
A distributed rough set theory based algorithm for an efficient big data pre-processing under the spark framework ZC Dagdia, C Zarges, G Beck, M Lebbah 2017 IEEE International Conference on Big Data (Big Data), 911-916, 2017 | 23 | 2017 |
A scalable and effective rough set theory-based approach for big data pre-processing Z Chelly Dagdia, C Zarges, G Beck, M Lebbah Knowledge and Information Systems 62 (8), 3321-3386, 2020 | 22 | 2020 |
Mixed Topological Map. M Lebbah, A Chazottes, F Badran, S Thiria ESANN 17, 47, 2005 | 22 | 2005 |
Deep embedded SOM: joint representation learning and self-organization F Forest, M Lebbah, H Azzag, J Lacaille The European Symposium on Artificial Neural Networks, 6, 2019 | 21* | 2019 |
G-stream: Growing neural gas over data stream M Ghesmoune, H Azzag, M Lebbah Neural Information Processing: 21st International Conference, ICONIP 2014 …, 2014 | 19 | 2014 |
Deep architectures for joint clustering and visualization with self-organizing maps F Forest, M Lebbah, H Azzag, J Lacaille Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2019 …, 2019 | 18 | 2019 |
A probabilistic self-organizing map for binary data topographic clustering M Lebbah, Y Bennani, N Rogovschi International Journal of Computational Intelligence and Applications 7 (04 …, 2008 | 18 | 2008 |