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2020 – today
- 2024
[j43]Chakib Fettal
, Lazhar Labiod, Mohamed Nadif:
Boosting Subspace Co-Clustering via Bilateral Graph Convolution. IEEE Trans. Knowl. Data Eng. 36(3): 960-971 (2024)- [j42]Lazhar Labiod, Mohamed Nadif:
Power Attributed Graph Embedding and Clustering. IEEE Trans. Neural Networks Learn. Syst. 35(1): 1439-1444 (2024) - [c111]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
More Discriminative Sentence Embeddings via Semantic Graph Smoothing. EACL (2) 2024: 8-13 - [c110]Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif:
Modèles graphiques causaux interactifs pour les données textuelles. EGC 2024: 465-472 - [c109]Imed Keraghel, Stanislas Morbieu, Mohamed Nadif:
Beyond Words: A Comparative Analysis of LLM Embeddings for Effective Clustering. IDA (1) 2024: 205-216 - [c108]Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif:
WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data. WSDM 2024: 1046-1049 - [c107]Lazhar Labiod, Mohamed Nadif, Aghiles Salah:
WSDM 2024 Workshop on Representation Learning & Clustering. WSDM 2024: 1214-1215 - [i7]Imed Keraghel, Stanislas Morbieu, Mohamed Nadif:
A survey on recent advances in named entity recognition. CoRR abs/2401.10825 (2024) - [i6]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Graph Cuts with Arbitrary Size Constraints Through Optimal Transport. CoRR abs/2402.04732 (2024) - [i5]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Scalable Multi-view Clustering via Explicit Kernel Features Maps. CoRR abs/2402.04794 (2024) - [i4]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
More Discriminative Sentence Embeddings via Semantic Graph Smoothing. CoRR abs/2402.12890 (2024) - [i3]Imed Keraghel, Mohamed Nadif:
Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering. CoRR abs/2412.14867 (2024) - 2023
- [j41]Paul Riverain, Simon Fossier, Mohamed Nadif:
Poisson degree corrected dynamic stochastic block model. Adv. Data Anal. Classif. 17(1): 135-162 (2023) - [c106]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Scalable Attributed-Graph Subspace Clustering. AAAI 2023: 7559-7567 - [c105]Mira Ait Saada, Mohamed Nadif:
Is Anisotropy Truly Harmful? A Case Study on Text Clustering. ACL (2) 2023: 1194-1203 - [c104]Mira Ait Saada, Mohamed Nadif:
Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora. EACL 2023: 1384-1395 - [c103]Mira Ait Saada, Mohamed Nadif:
Etude approfondie des représentations de données textuelles dans l'apprentissage non supervisé. EGC 2023: 361-368 - [c102]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Subspace Co-clustering avec Convolution Bilatérale sur Graphe. EGC 2023: 475-482 - [c101]Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Biclustering Basé sur le Transport Optimal. EGC 2023: 539-546 - [c100]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 - [c99]Mira Ait Saada, Mohamed Nadif:
Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study. IDA 2023: 1-14 - [c98]Louis Falissard, Séverine Affeldt, Mohamed Nadif:
Attentive Perturbation: Extending Prefix Tuning to Large Language Models Inner Representations. LOD (1) 2023: 488-496 - [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:
Clustering of contingency table and mixture model. Eur. J. Oper. Res. 183(3): 1055-1066 (2007) - [j5]François-Xavier Jollois, Mohamed Nadif:
Speed-up for the expectation-maximization algorithm for clustering categorical data. J. Glob. Optim. 37(4): 513-525 (2007) - [c18]François-Xavier Jollois, Mohamed Nadif:
Clustering and assessing the number of clusters with accelerated EM variant. RCIS 2007: 379-384 - 2006
- [j4]Gérard Govaert, Reiner Hähnle, Mohamed Nadif:
Preface. Soft Comput. 10(5): 405-405 (2006) - [j3]Gérard Govaert, Mohamed Nadif:
Fuzzy clustering to estimate the parameters of block mixture models. Soft Comput. 10(5): 415-422 (2006) - [c17]Rodolphe Priam, Mohamed Nadif:
Carte auto-organisatrice probabiliste sur données binaires. EGC 2006: 445-456 - [c16]Gérard Govaert, Mohamed Nadif:
Classification d'un tableau de contingence et modèle probabiliste. EGC 2006: 457-462 - [c15]Rodolphe Priam, Mohamed Nadif:
Probabilistic Enhanced Mapping with the Generative Tabular Model. ICDM 2006: 1021-1025 - [c14]Rodolphe Priam, Mohamed Nadif, François-Xavier Jollois:
Pixel-Based Visualization and Density-Based Tabular Model. VIEW 2006: 110-118 - 2005
- [j2]Gérard Govaert, Mohamed Nadif:
An EM Algorithm for the Block Mixture Model. IEEE Trans. Pattern Anal. Mach. Intell. 27(4): 643-647 (2005) - [c13]Mohamed Nadif, Gérard Govaert:
Block clustering via the block GEM and two-way EM algorithms. AICCSA 2005: 32 - [c12]François-Xavier Jollois, Mohamed Nadif:
Evaluation des algorithmes LEM et eLEM pour données continues. EGC 2005: 159-164 - [c11]Gérard Govaert, Mohamed Nadif:
Classification d'un tableau de contingence et modèle probabiliste. EGC 2005: 213-218 - [c10]Mohamed Nadif, Gérard Govaert:
Block Clustering of Contingency Table and Mixture Model. IDA 2005: 249-259 - [c9]Mohamed Nadif, Gérard Govaert:
A Comparison Between Block CEM and Two-Way CEM Algorithms to Cluster a Contingency Table. PKDD 2005: 609-616 - 2004
- [c8]François-Xavier Jollois, Mohamed Nadif:
Identification de blocs homogènes sur des données continues. EGC 2004: 241-246 - [c7]Mohamed Nadif, François-Xavier Jollois:
Accélération de EM pour données qualitatives : étude comparative de différentes versions. EGC 2004: 253-264 - [c6]François-Xavier Jollois, Mohamed Nadif:
Identification of homogeneous blocks in large binary data sets. Neural Networks and Computational Intelligence 2004: 60-65 - 2003
- [j1]Gérard Govaert, Mohamed Nadif:
Clustering with block mixture models. Pattern Recognit. 36(2): 463-473 (2003) - [c5]François-Xavier Jollois, Mohamed Nadif, Gérard Govaert:
Classification croisée sur des données binaires de grande taille. EGC 2003: 213-218 - 2002
- [c4]Mohamed Nadif, Gérard Govaert:
Parameters Estimation of Block Mixture Models. COMPSTAT 2002: 491-496 - [c3]François-Xavier Jollois, Mohamed Nadif:
Evaluation du nombre de classes dans un cadre bayésien. EGC 2002: 357-362 - [c2]François-Xavier Jollois, Mohamed Nadif:
Clustering Large Categorical Data. PAKDD 2002: 257-263 - 2001
- [c1]François-Xavier Jollois, Mohamed Nadif, Franck Marchetti:
Classification de données qualitatives. EGC 2001: 369
1990 – 1999
- 1991
- [b1]Mohamed Nadif:
Classification automatique et données manquantes. (Clustering and missing data). University of Metz, France, 1991
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
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Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
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Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
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last updated on 2025-01-24 17:15 CET by the dblp team
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