| Assessing a mixture model for clustering with the integrated completed likelihood C Biernacki, G Celeux, G Govaert IEEE transactions on pattern analysis and machine intelligence 22 (7), 719-725, 2002 | 2066 | 2002 |
| Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models C Biernacki, G Celeux, G Govaert Computational Statistics & Data Analysis 41 (3-4), 561-575, 2003 | 856 | 2003 |
| The morphology of built-up landscapes in Wallonia (Belgium): A classification using fractal indices I Thomas, P Frankhauser, C Biernacki Landscape and urban planning 84 (2), 99-115, 2008 | 254 | 2008 |
| Model-based cluster and discriminant analysis with the MIXMOD software C Biernacki, G Celeux, G Govaert, F Langrognet Computational Statistics & Data Analysis 51 (2), 587-600, 2006 | 224 | 2006 |
| An improvement of the NEC criterion for assessing the number of clusters in a mixture model C Biernacki, G Celeux, G Govaert Pattern Recognition Letters 20 (3), 267-272, 1999 | 204 | 1999 |
| Using the classification likelihood to choose the number of clusters C Biernacki, G Govaert Computing Science and Statistics, 451-457, 1997 | 195 | 1997 |
| Choosing models in model-based clustering and discriminant analysis C Biernacki, G Govaert Journal of Statistical Computation and Simulation 64 (1), 49-71, 1999 | 181 | 1999 |
| Rmixmod: The R package of the model-based unsupervised, supervised, and semi-supervised classification Mixmod library R Lebret, S Iovleff, F Langrognet, C Biernacki, G Celeux, G Govaert Journal of Statistical Software 67, 1-29, 2015 | 119 | 2015 |
| Mixture of Gaussians for distance estimation with missing data E Eirola, A Lendasse, V Vandewalle, C Biernacki Neurocomputing 131, 32-42, 2014 | 79 | 2014 |
| A generative model for rank data based on insertion sort algorithm C Biernacki, J Jacques Computational Statistics & Data Analysis 58, 162-176, 2013 | 65 | 2013 |
| Exact and Monte Carlo calculations of integrated likelihoods for the latent class model C Biernacki, G Celeux, G Govaert Journal of Statistical Planning and Inference 140 (11), 2991-3002, 2010 | 65 | 2010 |
| Model-based clustering of Gaussian copulas for mixed data M Marbac, C Biernacki, V Vandewalle Communications in Statistics-Theory and Methods 46 (23), 11635-11656, 2017 | 60 | 2017 |
| A multifractal mass transference principle for Gibbs measures with applications to dynamical Diophantine approximation AH Fan, J Schmeling, S Troubetzkoy Proceedings of the London Mathematical Society 107 (5), 1173-1219, 2013 | 59 | 2013 |
| Model-based co-clustering for ordinal data J Jacques, C Biernacki Computational Statistics & Data Analysis 123, 101-115, 2018 | 58 | 2018 |
| Degeneracy in the maximum likelihood estimation of univariate Gaussian mixtures with EM C Biernacki, S Chrétien Statistics & probability letters 61 (4), 373-382, 2003 | 56 | 2003 |
| L'épreuve des inégalités H Lagrange PUF, 2015 | 53 | 2015 |
| Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm C Biernacki, J Jacques Statistics and Computing 26, 929-943, 2016 | 52 | 2016 |
| Model-based clustering for multivariate partial ranking data J Jacques, C Biernacki Journal of Statistical Planning and Inference 149, 201-217, 2014 | 50 | 2014 |
| A generalized discriminant rule when training population and test population differ on their descriptive parameters C Biernacki, F Beninel, V Bretagnolle Biometrics 58 (2), 387-397, 2002 | 50 | 2002 |
| Initializing EM using the properties of its trajectories in Gaussian mixtures C Biernacki Statistics and Computing 14, 267-279, 2004 | 39 | 2004 |
Gilles CeleuxDirecteur de recherche InriaAdresse e-mail validée de inria.fr