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2020 – today
- 2023
[j41]Paul Riverain, Simon Fossier, Mohamed Nadif:
Poisson degree corrected dynamic stochastic block model. Adv. Data Anal. Classif. 17(1): 135-162 (2023)- [c103]Mira Ait Saada, Mohamed Nadif:
Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora. EACL 2023: 1384-1395 - [c102]Mira Ait Saada, Mohamed Nadif:
Etude approfondie des représentations de données textuelles dans l'apprentissage non supervisé. EGC 2023: 361-368 - [c101]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Subspace Co-clustering avec Convolution Bilatérale sur Graphe. EGC 2023: 475-482 - [c100]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Biclustering Basé sur le Transport Optimal. EGC 2023: 539-546 - [c99]Amine Ferdjaoui, Amira Tlati, Séverine Affeldt, Mohamed Nadif:
CORPEX : Analyse exploratoire d'un corpus biomédical à l'aide de la classification croisée. EGC 2023: 597-604 - [c98]Mira Ait Saada, Mohamed Nadif:
Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study. IDA 2023: 1-14 - [c97]Chakib Fettal
, Lazhar Labiod, Mohamed Nadif:
Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering. WSDM 2023: 303-311 - 2022
- [j40]Louis Geiler, Séverine Affeldt, Mohamed Nadif:
An effective strategy for churn prediction and customer profiling. Data Knowl. Eng. 142: 102100 (2022) - [j39]Louis Geiler, Séverine Affeldt, Mohamed Nadif:
A survey on machine learning methods for churn prediction. Int. J. Data Sci. Anal. 14(3): 217-242 (2022) - [j38]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
TensorClus: A python library for tensor (Co)-clustering. Neurocomputing 468: 464-468 (2022) - [j37]Mickael Febrissy, Aghiles Salah, Melissa Ailem, Mohamed Nadif:
Improving NMF clustering by leveraging contextual relationships among words. Neurocomputing 495: 105-117 (2022) - [j36]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
CAEclust: A Consensus of Autoencoders Representations for Clustering. Image Process. Line 12: 590-603 (2022) - [j35]Paul Riverain, Simon Fossier, Mohamed Nadif:
Semi-supervised Latent Block Model with pairwise constraints. Mach. Learn. 111(5): 1739-1764 (2022) - [c96]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Subspace Co-clustering with Two-Way Graph Convolution. CIKM 2022: 3938-3942 - [c95]Louis Geiler, Séverine Affeldt, Mohamed Nadif:
Apprentissage machine pour la prédiction de l'attrition: une étude comparative. EGC 2022: 135-146 - [c94]Mira Ait Saada, François Role, Mohamed Nadif:
Classification non supervisée de documents à partir des modèles Transformeurs. EGC 2022: 331-338 - [c93]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Apprentissage Joint de la Représentation et du Clustering avec un Réseau Convolutif sur Graphe. EGC 2022: 363-370 - [c92]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Efficient and Effective Optimal Transport-Based Biclustering. NeurIPS 2022 - [c91]Rafika Boutalbi, Mira Ait Saada, Anastasiia Iurshina, Steffen Staab, Mohamed Nadif:
Tensor-based Graph Modularity for Text Data Clustering. SIGIR 2022: 2227-2231 - [c90]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Efficient Graph Convolution for Joint Node Representation Learning and Clustering. WSDM 2022: 289-297 - 2021
- [j34]Lazhar Labiod, Mohamed Nadif:
Efficient regularized spectral data embedding. Adv. Data Anal. Classif. 15(1): 99-119 (2021) - [j33]Mohamed Nadif, François Role:
Unsupervised and self-supervised deep learning approaches for biomedical text mining. Briefings Bioinform. 22(2): 1592-1603 (2021) - [j32]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
Implicit consensus clustering from multiple graphs. Data Min. Knowl. Discov. 35(6): 2313-2340 (2021) - [j31]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Regularized bi-directional co-clustering. Stat. Comput. 31(3): 32 (2021) - [c89]Mira Ait Saada, François Role, Mohamed Nadif:
How to Leverage a Multi-layered Transformer Language Model for Text Clustering: an Ensemble Approach. CIKM 2021: 2837-2841 - [c88]Paul Riverain, Simon Fossier, Mohamed Nadif:
Modèle à Blocs Stochastiques corrigé en degrés pour des graphes dynamiques. EGC 2021: 349-356 - [c87]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Approche ensemble pour le co-clustering par blocs sur des données textuelles: Application au biomédical. EGC 2021: 381-388 - [c86]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Méthode ensemble de clustering profond. EGC 2021: 413-420 - [c85]Mira Ait Saada, François Role, Mohamed Nadif:
Unsupervised Methods for the Study of Transformer Embeddings. IDA 2021: 287-300 - [c84]Paul Riverain, Simon Fossier, Mohamed Nadif:
Model-based Poisson co-clustering for Attributed Networks. ICDM (Workshops) 2021: 703-710 - [c83]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Regularized Dual-PPMI Co-clustering for Text Data. SIGIR 2021: 2263-2267 - 2020
- [j30]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
Tensor latent block model for co-clustering. Int. J. Data Sci. Anal. 10(2): 161-175 (2020) - [j29]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Spectral clustering via ensemble deep autoencoder learning (SC-EDAE). Pattern Recognit. 108: 107522 (2020) - [c82]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Ensemble Block Co-clustering: A Unified Framework for Text Data. CIKM 2020: 5-14 - [c81]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
Défi EGC 2020 : Analyse tensorielle de données issues de la conférence EGC. EGC 2020: 217-228 - [c80]Mickael Febrissy, Mohamed Nadif:
A Consensus Approach to Improve NMF Document Clustering. IDA 2020: 171-183 - [c79]Mickael Febrissy, Mohamed Nadif:
Wasserstein Embeddings for Nonnegative Matrix Factorization. LOD (1) 2020: 309-321
2010 – 2019
- 2019
- [j28]Aghiles Salah, Mohamed Nadif:
Directional co-clustering. Adv. Data Anal. Classif. 13(3): 591-620 (2019) - [c78]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
Co-clustering from Tensor Data. PAKDD (1) 2019: 370-383 - [c77]Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif:
Sparse Tensor Co-clustering as a Tool for Document Categorization. SIGIR 2019: 1157-1160 - [i2]Séverine Affeldt, Lazhar Labiod, Mohamed Nadif:
Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE). CoRR abs/1901.02291 (2019) - 2018
- [j27]Gérard Govaert, Mohamed Nadif:
Mutual information, phi-squared and model-based co-clustering for contingency tables. Adv. Data Anal. Classif. 12(3): 455-488 (2018) - [j26]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Simultaneous Spectral Data Embedding and Clustering. IEEE Trans. Neural Networks Learn. Syst. 29(12): 6396-6401 (2018) - [c76]Aghiles Salah, Melissa Ailem, Mohamed Nadif:
Word Co-Occurrence Regularized Non-Negative Matrix Tri-Factorization for Text Data Co-Clustering. AAAI 2018: 3992-3999 - [c75]François Role, Stanislas Morbieu, Mohamed Nadif:
Unsupervised Evaluation of Text Co-clustering Algorithms Using Neural Word Embeddings. CIKM 2018: 1827-1830 - [c74]Melissa Ailem, François Role, Mohamed Nadif:
Sparse Poisson Latent Block Model for Document Clustering (Extended Abstract). ICDE 2018: 1743-1744 - [c73]Blaise Hanczar, Mohamed Nadif:
Controlling and Visualizing the Precision-Recall Tradeoff for External Performance Indices. ECML/PKDD (1) 2018: 687-702 - 2017
- [j25]Aghiles Salah, Mohamed Nadif:
Social regularized von Mises-Fisher mixture model for item recommendation. Data Min. Knowl. Discov. 31(5): 1218-1241 (2017) - [j24]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Multi-manifold matrix decomposition for data co-clustering. Pattern Recognit. 64: 386-398 (2017) - [j23]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Erratum to 'Multi-Manifold Matrix Decomposition for Data Co-clustering' Pattern Recognition 64 (2017) 386-398. Pattern Recognit. 69: 352-353 (2017) - [j22]Melissa Ailem, François Role, Mohamed Nadif:
Model-based co-clustering for the effective handling of sparse data. Pattern Recognit. 72: 108-122 (2017) - [j21]Charlotte Laclau, Mohamed Nadif:
Diagonal latent block model for binary data. Stat. Comput. 27(5): 1145-1163 (2017) - [j20]Kais Allab, Lazhar Labiod, Mohamed Nadif:
A Semi-NMF-PCA Unified Framework for Data Clustering. IEEE Trans. Knowl. Data Eng. 29(1): 2-16 (2017) - [j19]Melissa Ailem, François Role, Mohamed Nadif:
Sparse Poisson Latent Block Model for Document Clustering. IEEE Trans. Knowl. Data Eng. 29(7): 1563-1576 (2017) - [c72]Aghiles Salah, Melissa Ailem, Mohamed Nadif:
A Way to Boost Semi-NMF for Document Clustering. CIKM 2017: 2275-2278 - [c71]Nicolas Médoc, Mohammad Ghoniem, Mohamed Nadif:
Analyse exploratoire de corpus textuels pour le journalisme d'investigation. EGC 2017: 477-480 - [c70]Milad Leyli-Abadi, Lazhar Labiod, Mohamed Nadif:
Denoising Autoencoder as an Effective Dimensionality Reduction and Clustering of Text Data. PAKDD (2) 2017: 801-813 - [c69]Aghiles Salah, Mohamed Nadif:
Model-based von Mises-Fisher Co-clustering with a Conscience. SDM 2017: 246-254 - [c68]Melissa Ailem, Aghiles Salah, Mohamed Nadif:
Non-negative Matrix Factorization Meets Word Embedding. SIGIR 2017: 1081-1084 - 2016
- [j18]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
Generalized topographic block model. Neurocomputing 173: 442-449 (2016) - [j17]Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif:
A dynamic collaborative filtering system via a weighted clustering approach. Neurocomputing 175: 206-215 (2016) - [j16]Charlotte Laclau, Mohamed Nadif:
Hard and fuzzy diagonal co-clustering for document-term partitioning. Neurocomputing 193: 133-147 (2016) - [j15]Melissa Ailem, François Role, Mohamed Nadif, Florence Demenais:
Unsupervised text mining for assessing and augmenting GWAS results. J. Biomed. Informatics 60: 252-259 (2016) - [j14]Melissa Ailem, François Role, Mohamed Nadif:
Graph modularity maximization as an effective method for co-clustering text data. Knowl. Based Syst. 109: 160-173 (2016) - [j13]Rodolphe Priam, Mohamed Nadif:
Data visualization via latent variables and mixture models: a brief survey. Pattern Anal. Appl. 19(3): 807-819 (2016) - [c67]Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif:
Model-based Co-clustering for High Dimensional Sparse Data. AISTATS 2016: 866-874 - [c66]Kais Allab, Lazhar Labiod, Mohamed Nadif:
SemiNMF-PCA framework for Sparse Data Co-clustering. CIKM 2016: 347-356 - [c65]Nicolas Médoc, Mohammad Ghoniem, Mohamed Nadif:
Vers une approche Visual Analytics pour explorer les variantes de sujets d'un corpus. EGC 2016: 539-540 - [c64]Lazhar Labiod, Mohamed Nadif:
Bi-stochastic Matrix Approximation Framework for Data Co-clustering. IDA 2016: 273-283 - [c63]Nicolas Médoc, Mohammad Ghoniem, Mohamed Nadif:
Visual exploration of topic variants through a hybrid biclustering approach. IHM 2016: 103-114 - [c62]Nicolas Médoc, Mohammad Ghoniem, Mohamed Nadif:
Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering. ECML/PKDD (3) 2016: 59-62 - [c61]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Power Simultaneous Spectral Data Embedding and Clustering. SDM 2016: 270-278 - [c60]Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif:
Stochastic Co-clustering for Document-Term Data. SDM 2016: 306-314 - 2015
- [j12]Lazhar Labiod, Mohamed Nadif:
A Unified Framework for Data Visualization and Coclustering. IEEE Trans. Neural Networks Learn. Syst. 26(9): 2194-2199 (2015) - [c59]Melissa Ailem, François Role, Mohamed Nadif:
Co-clustering Document-term Matrices by Direct Maximization of Graph Modularity. CIKM 2015: 1807-1810 - [c58]Blaise Hanczar, Mohamed Nadif:
Compromis précision-rappel dans l'évaluation des performances. EGC 2015: 113-124 - [c57]Aghiles Salah, Nicoleta Rogovschi, François Role, Mohamed Nadif:
Pour une meilleure exploitation de la classification croisée dans les systèmes de filtrage collaboratif. EGC 2015: 347-358 - [c56]Charlotte Laclau, Francisco de A. T. de Carvalho, Mohamed Nadif:
Fuzzy co-clustering with automated variable weighting. FUZZ-IEEE 2015: 1-8 - [c55]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Simultaneous Semi-NMF and PCA for Clustering. ICDM 2015: 679-684 - [c54]Mohamed-Cherif Dani, François-Xavier Jollois, Mohamed Nadif, Cassiano Freixo:
Adaptive Threshold for Anomaly Detection Using Time Series Segmentation. ICONIP (3) 2015: 82-89 - [c53]Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif:
An Efficient Incremental Collaborative Filtering System. ICONIP (3) 2015: 375-383 - [c52]Kais Allab, Lazhar Labiod, Mohamed Nadif:
Multi-Manifold Matrix Tri-Factorization for Text Data Clustering. ICONIP (1) 2015: 705-715 - [c51]Charlotte Laclau, Mohamed Nadif:
Diagonal Co-clustering Algorithm for Document-Word Partitioning. IDA 2015: 170-180 - 2014
- [j11]François Role, Mohamed Nadif:
Beyond cluster labeling: Semantic interpretation of clusters' contents using a graph representation. Knowl. Based Syst. 56: 141-155 (2014) - [j10]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
Topographic Bernoulli block mixture mapping for binary tables. Pattern Anal. Appl. 17(4): 839-847 (2014) - [c50]Blaise Hanczar, Mohamed Nadif:
Aggregation of Biclustering Solutions for Ensemble Approach. ICPRAM (Selected Papers) 2014: 19-34 - [c49]Blaise Hanczar, Mohamed Nadif:
Unsupervised Consensus Functions Applied to Ensemble Biclustering. ICPRAM 2014: 30-39 - [c48]Charlotte Laclau, Mohamed Nadif:
Fast Simultaneous Clustering and Feature Selection for Binary Data. IDA 2014: 192-202 - [c47]Nicolas Médoc, Mickaël Stefas, Mohammad Ghoniem, Mohamed Nadif:
Visual analytics of text streams through multiple dynamic frequency matrices. IEEE VAST 2014: 381-382 - 2013
- [c46]Blaise Hanczar, Mohamed Nadif:
Precision-recall space to correct external indices for biclustering. ICML (2) 2013: 136-144 - [c45]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
Gaussian Topographic Co-clustering Model. IDA 2013: 345-356 - [c44]Nicoleta Rogovschi, Lazhar Labiod, Mohamed Nadif:
A topographical nonnegative matrix factorization algorithm. IJCNN 2013: 1-6 - [i1]Iheb Ben Amor, Athman Bouguettaya, Mourad Ouziri, Salima Benbernou, Mohamed Nadif:
Data Leak Aware Crowdsourcing in Social Network. CoRR abs/1305.6451 (2013) - 2012
- [j9]Blaise Hanczar, Mohamed Nadif:
Ensemble methods for biclustering tasks. Pattern Recognit. 45(11): 3938-3949 (2012) - [c43]Nicoleta Rogovschi, Mohamed Nadif:
Classification topologique probabiliste pour des données catégorielles. EGC 2012: 179-188 - [c42]Nicoleta Rogovschi, Lazhar Labiod, Mohamed Nadif:
Un Algorithme Spectral pour le Co-clustering Topographique. EGC 2012: 579-580 - [c41]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
Nonlinear Mapping by Constrained Co-clustering. ICPRAM (1) 2012: 63-68 - [c40]Rodolphe Priam, Mohamed Nadif:
Generative Topographic Mapping and Factor Analyzers. ICPRAM (1) 2012: 284-287 - [c39]Nicoleta Rogovschi, Lazhar Labiod, Mohamed Nadif:
A spectral algorithm for topographical Co-clustering. IJCNN 2012: 1-6 - [c38]Iheb Ben Amor, Salima Benbernou, Mourad Ouziri, Mohamed Nadif, Athman Bouguettaya:
Data Leak Aware Crowdsourcing in Social Network. WISE Workshops 2012: 226-236 - 2011
- [j8]Blaise Hanczar, Mohamed Nadif:
Using the bagging approach for biclustering of gene expression data. Neurocomputing 74(10): 1595-1605 (2011) - [c37]Blaise Hanczar, Mohamed Nadif:
Improving the Biological Relevance of Biclustering for Microarray Data in Using Ensemble Methods. DEXA Workshops 2011: 413-417 - [c36]W. Parr Bouberima, Y. Khemal Bencheikh, Mohamed Nadif:
Different Variants of Normalized EM Algorithm for Gene Expression Data. DEXA Workshops 2011: 418-422 - [c35]Haifa Ben Saber, Mourad Elloumi, Mohamed Nadif:
Block Mixture Model for the Biclustering of Microarray Data. DEXA Workshops 2011: 423-427 - [c34]François Role, Mohamed Nadif:
PPMI : étude formelle d'une variante à valeurs positives de la PMI. EGC 2011: 295-296 - [c33]François Role, Mohamed Nadif:
Handling the Impact of Low Frequency Events on Co-occurrence based Measures of Word Similarity - A Case Study of Pointwise Mutual Information. KDIR 2011: 226-231 - [c32]Lazhar Labiod, Mohamed Nadif:
Co-clustering for Binary and Categorical Data with Maximum Modularity. ICDM 2011: 1140-1145 - [c31]Nicoleta Rogovschi, Mohamed Nadif:
Weighted Topological Clustering for Categorical Data. ICONIP (1) 2011: 599-607 - [c30]Lazhar Labiod, Mohamed Nadif:
Co-clustering for Binary Data with Maximum Modularity. ICONIP (2) 2011: 700-708 - [c29]Lazhar Labiod, Mohamed Nadif:
Co-clustering under Nonnegative Matrix Tri-Factorization. ICONIP (2) 2011: 709-717 - 2010
- [c28]Blaise Hanczar, Mohamed Nadif:
Bagged Biclustering for Microarray Data. ECAI 2010: 1131-1132 - [c27]Mohamed Nadif, Gérard Govaert:
Model-Based Co-clustering for Continuous Data. ICMLA 2010: 175-180 - [c26]Faryel Allouti, Mohamed Nadif, Benoît Otjacques:
Visualization of Document Clusters - An Interactive Visual Tool to Browse Textual Documents. IMAGAPP/IVAPP 2010: 157-160 - [c25]Faryel Allouti, Mohamed Nadif, Benoît Otjacques:
Multinomial Self Organizing Maps. ISDA 2010: 621-626 - [c24]Blaise Hanczar, Mohamed Nadif:
Bagging for Biclustering: Application to Microarray Data. ECML/PKDD (1) 2010: 490-505
2000 – 2009
- 2009
- [c23]Faryel Allouti, Mohamed Nadif, Le Thi Hoai An, Benoît Otjacques:
Mixture Model and MDSDCA for Textual Data. CDVE 2009: 240-244 - 2008
- [j7]Gérard Govaert, Mohamed Nadif:
Block clustering with Bernoulli mixture models: Comparison of different approaches. Comput. Stat. Data Anal. 52(6): 3233-3245 (2008) - [c22]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
The Block Generative Topographic Mapping. ANNPR 2008: 13-23 - [c21]Mohamed Nadif, Gérard Govaert:
Algorithms for Model-based Block Gaussian Clustering. DMIN 2008: 536-542 - [c20]Rodolphe Priam, Mohamed Nadif, Gérard Govaert:
Binary Block GTM : Carte auto-organisatrice probabiliste pour les grands tableaux binaires. EGC 2008: 265-272 - [c19]Faryel Allouti, Mohamed Nadif, Benoît Otjacques, Le Thi Hoai An:
Visualisation du parcours des fichiers attachés aux messages électroniques. IHM 2008: 29-32 - 2007
- [j6]Gérard Govaert, Mohamed Nadif: