| Clustering with block mixture models G Govaert, M Nadif Pattern Recognition 36 (2), 463-473, 2003 | 327 | 2003 |
| Block clustering with Bernoulli mixture models: Comparison of different approaches G Govaert, M Nadif Computational Statistics & Data Analysis 52 (6), 3233-3245, 2008 | 280 | 2008 |
| Co-clustering: models, algorithms and applications G Govaert, M Nadif John Wiley & Sons, 2013 | 257 | 2013 |
| Classification, clustering, and data analysis: recent advances and applications K Jajuga, A Sokolowski, HH Bock Springer Science & Business Media, 2012 | 167 | 2012 |
| An EM algorithm for the block mixture model G Govaert, M Nadif IEEE Transactions on Pattern Analysis and machine intelligence 27 (4), 643-647, 2005 | 143 | 2005 |
| Latent block model for contingency table G Govaert, M Nadif Communications in Statistics—Theory and Methods 39 (3), 416-425, 2010 | 120 | 2010 |
| Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of … D Banks, L House, FR McMorris, P Arabie, WA Gaul Springer Science & Business Media, 2011 | 116 | 2011 |
| A dynamic collaborative filtering system via a weighted clustering approach A Salah, N Rogovschi, M Nadif Neurocomputing 175, 206-215, 2016 | 109 | 2016 |
| Spectral clustering via ensemble deep autoencoder learning (SC-EDAE) S Affeldt, L Labiod, M Nadif Pattern Recognition 108, 107522, 2020 | 105 | 2020 |
| A semi-NMF-PCA unified framework for data clustering K Allab, L Labiod, M Nadif IEEE Transactions on Knowledge and Data Engineering 29 (1), 2-16, 2016 | 100 | 2016 |
| Data analysis G Govaert | 79 | 2013 |
| Handling the impact of low frequency events on co-occurrence based measures of word similarity-a case study of pointwise mutual information F Role, M Nadif International conference on knowledge discovery and information retrieval 2 …, 2011 | 76 | 2011 |
| Unsupervised and self-supervised deep learning approaches for biomedical text mining M Nadif, F Role Briefings in Bioinformatics 22 (2), 1592-1603, 2021 | 74 | 2021 |
| Ensemble methods for biclustering tasks B Hanczar, M Nadif Pattern Recognition 45 (11), 3938-3949, 2012 | 68 | 2012 |
| Co-clustering document-term matrices by direct maximization of graph modularity M Ailem, F Role, M Nadif Proceedings of the 24th ACM international on conference on information and …, 2015 | 65 | 2015 |
| Sparse poisson latent block model for document clustering M Ailem, F Role, M Nadif IEEE Transactions on Knowledge and Data Engineering 29 (7), 1563-1576, 2017 | 60 | 2017 |
| A survey on machine learning methods for churn prediction L Geiler, S Affeldt, M Nadif International Journal of Data Science and Analytics 14 (3), 217-242, 2022 | 59 | 2022 |
| Coclust: a python package for co-clustering F Role, S Morbieu, M Nadif Journal of Statistical Software 88, 1-29, 2019 | 59 | 2019 |
| Co-clustering for binary and categorical data with maximum modularity L Labiod, M Nadif 2011 IEEE 11th international conference on data mining, 1140-1145, 2011 | 58 | 2011 |
| Word co-occurrence regularized non-negative matrix tri-factorization for text data co-clustering A Salah, M Ailem, M Nadif Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 55 | 2018 |
