BIERNACKI Christophe


Professor at University Lille 1, Laboratory of Mathematics, UMR CNRS 8524

Scientific leader of the mΘdal team at INRIA Lille Nord-Europe


Quick access...

News

Research Interests

The MIXMOD software

Other softwares

Preprints

Books

Journal papers

Technical papers



Address: Université Lille 1 - UFR de Mathématiques - Cité Scientifique - 59655 Villeneuve d'Ascq Cedex - FRANCE

Email: Christophe.Biernacki@{math.univ-lille1,inria}.fr

Phone: +33 3 20 43 68 76 / +33 3 59 57 78 58

Fax: +33 3 20 43 43 02



News

One-day meeting
"Statistique et données massives : enjeux et perspectives"

Monday October 13th at UPMC, Jussieu Campus (Paris)

M. Jordan (Université de Berkeley)
S. Mallat (ENS, Académie des Sciences)
P. Tassi (Médiamétrie)
H. Verdier (Etalab, Ministère)

Information and registration HERE





Research interests

  • Model-based classification and clustering

  • Mixture models

  • EM algorithm

  • Model selection

  • Biological applications



The MIXMOD software

The MIXMOD (MIXture MODelling) software fits mixture models (Gaussian, Bernoulli, multinomial) to a given data set described by continuous or categorical variables with either a clustering or a discriminant analysis purpose. It is publicly available under the GPL license and is distributed for different platforms (Linux, Unix, Windows). It is developed jointly by INRIA Saclay Île-de-France (SELECT project), the laboratory of mathematics of Besançon, the laboratory of Mathematics of University Lille 1 and the Heudiasyc laboratory of Compiègne.

The software, the statistical documentation and also the userguide are available HERE on the internet.

Documents of MIXMOD previous One Day Conferences
October 2006, December 2008, December 2010, September 2013



Other softwares

  • The blockcluster package blockcluster is a R package for model-based simultaneous clustering of rows and columns. It is available online on CRAN HERE for all major platforms (Linux, MacOS, Windows). This package allows to co-cluster binary, contingency and continuous data. It also comes with utility functions to visualize the data. This package is developed by INRIA (mΘdal team) in collaboration with University of Technology of Compiègne. A short tutorial for the package can be downloaded from here.
  • The rankclust package rankclust is a R package for model-based clustering of partial multivariate rank data. It is available online HERE . This package is developed by INRIA ( mΘdal team). A description of the underlying model is available in the technical paper Preprint HAL n°00743384.



Preprints

  • M. Marbac, C. Biernacki & V. Vandewalle (2014). Model-based clustering of Gaussian copulas for mixed data. Preprint. Preprint HAL n°00987760

  • L. Yengo, J. Jacques, C. Biernacki & M. Canouil (2014). Variable Clustering in High-Dimensional Linear Regression: The R Package clere. Preprint. PDF

  • C. Biernacki & G. Castellan (2011). A Data-Driven Bound on Variances for Avoiding Degeneracy in Univariate Gaussian Mixtures. Pub. IRMA Lille, Vol. 71-IV. PDF



Books

  • C. Biernacki (2015). Mixture models. Choix de modèles et agrégation, Sous la direction de J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition: Technip. (pdf bientôt disponible)

  • C. Biernacki & C. Maugis-Rabusseau (2015). High-dimensional clustering. Choix de modèles et agrégation, Sous la direction de J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition: Technip. (pdf bientôt disponible)

  • F. Beninel, C. Biernacki, C. Bouveyron, J. Jacques & A. Lourme (2012). Parametric link models for knowledge transfer in statistical learning. Knowledge Transfer: Practices, Types and Challenges, chez Nova Publishers, 40 pages, ISBN: 978-1-62081-579-3. PDF



Journal papers

  • M. Marbac, C. Biernacki & V. Vandewalle (2016). Finite mixture model of conditional dependencies modes to cluster categorical data. Advances in Data Analysis and Classification, in press. Preprint HAL n°00950112,

  • C. Biernacki & J. Jacques (2015). Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm. Statistics and Computing, in press. Preprint HAL n°01052447

  • R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux & G. Govaert (2015). Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library. Journal of Statistical Software, in press. PDF

  • M. Marbac, C. Biernacki & V. Vandewalle (2014). Model-based clustering for conditionally correlated categorical data. Journal of Classification, in press. Preprint HAL n°00787757

  • J. Jacques, Q. Grimonprez & C. Biernacki (2014). Rankcluster: An R Package for clustering multivariate partial ranking. The R Journal, in press. PDF

  • J.Jacques & C.Biernacki (2014). Model-based clustering for multivariate partial ranking data. Journal of Statistical and Planning Inference, 149, 201–217. Preprint HAL n°00743384

  • L. Yengo, J.Jacques & C.Biernacki (2013). Variable clustering in high dimensional linear regression models. Journal de la SFdS, in press. Preprint HAL n°00764927

  • E. Eirola, A. Lendasse, V. Vandewalle & C. Biernacki (2014). Mixture of Gaussians for Distance Estimation with Missing Data. Neurocomputing, 131, 32–42. PDF

  • C. Biernacki & A. Lourme (2013). Gaussian Parsimonious Clustering Models Scale Invariant and Stable by Projection. Statistics and Computing, in press. PDF

  • V. Vandewalle, C. Biernacki, G. Celeux & G. Govaert (2013). A predictive deviance criterion for selecting a generative model in semi-supervised classification. Computational Statistics and Data Analysis, 64, 220-236. PS

  • C. Biernacki & J. Jacques (2013). A generative model for rank data based on insertion sort algorithm, Computational Statistics and Data Analysis, 58, 162-176. PDF

  • A. Lourme & C. Biernacki (2013). Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins, Computational Statistics, 28(1), 371-391. PDF

  • A. Lourme & C. Biernacki (2011). Classification simultanée de plusieurs échantillons sous contrainte d’égalité des entropies de partition. Journal de la Société Française de Statistique, 152(3), 21–33. PDF

  • A. Lourme & C. Biernacki (2011). Simultaneous t-Model-Based Clustering for Data Differing over Time Period: Application for Understanding Companies Financial Health. Case Studies in Business, Industry and Government Statistics (CSBIGS), 4(2), 73–82. PDF

  • C. Biernacki, G. Celeux & G. Govaert (2010). Exact and Monte Carlo Calculations of Integrated Likelihoods for the Latent Class Model. Journal of Statistical Planning and Inference, 140(11), 2991-3002. PDF

  • J. Jacques & C. Biernacki (2010). Extension of model-based classification for binary data when training and test populations differ. Journal of Applied Statistics, 37(5), 749-766. PDF

  • C. Biernacki (2009). Pourquoi les modèles de mélange pour la classification ? La Revue de Modulad, 40, 1-22. PDF

  • I. Thomas, P. Frankhauser & C. Biernacki (2008). The morphology of built-up landscapes in Wallonia (Belgium): a classification using fractal indices. Landscape and Urban Planning, 84, 99-115. PDF

  • C. Biernacki (2007). Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures for Grouped Data and Behaviour of the EM Algorithm. Journal of Scandinavian Statistics, 34, 569-586. PS

  • J. Jacques & C. Biernacki (2007). Analyse discriminante sur données binaires lorsque les populations d’apprentissage et de test sont différentes. Revue des Nouvelles Technologies de l'Information, Data Mining et apprentissage statistique : application en assurance, banque et marketing, A1, 109-125. PDF

  • F. Beninel & C. Biernacki (2007). Modèles d’extension de la régression logistique. Revue des Nouvelles Technologies de l'Information, Data Mining et apprentissage statistique : application en assurance, banque et marketing, A1, 207-218. PDF

  • C. Biernacki, G. Celeux, A. Anwuli, G. Govaert & F. Langrognet (2006).Le logiciel MIXMOD d'analyse de mélange pour la classification et l'analyse discriminante. La Revue de Modulad, 35, 25-44. PDF

  • C. Biernacki, G. Celeux, G. Govaert & F. Langrognet (2006). Model-Based Cluster and Discriminant Analysis with the MIXMOD Software. Computational Statistics and Data Analysis, 51(2), 587-600. PS

  • C. Biernacki (2005). Testing for a Global Maximum of the Likelihood . Journal of Computational and Graphical Statistics, 14(3), 657-674. (PDF: paper , appendix)

  • C. Biernacki (2004). Initializing EM Using the Properties of its Trajectories in Gaussian Mixtures. Statistics and Computing, 14(3), 267-279. PS

  • C. Biernacki & S. Chrétien (2003). Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures with EM. Statistics & Probability Letters, 61, 373-382. PS

  • C. Biernacki, G. Celeux & G. Govaert (2003). Choosing Starting Values for the EM Algorithm for Getting the Highest Likelihood in Multivariate Gaussian Mixture Models. Computational Statistics and Data Analysis, 41, 561-575. PS

  • C. Biernacki, F. Beninel & V. Bretagnolle (2002). A Generalized Discriminant Rule when Training Population and Test Population Differ on their Descriptive Parameters. Biometrics, 58(2), 387-397. PS

  • C. Biernacki, G. Celeux & G. Govaert (2000). Assessing a Mixture Model for Clustering with the IntegratedCompleted Likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719-725. PS

  • C. Biernacki, G. Celeux & G. Govaert (1999). An Improvement of the NEC Criterion for Assessing the Number of Clusters in a Mixture Model. Pattern Recognition Letters, 20(3), 267-272. PS

  • C. Biernacki & G. Govaert (1999). Choosing Models in Model-based Clustering and Discriminant Analysis. Journal of Statistical Computation and Simulation, 64, 49-71. PS

  • C. Biernacki (1999). Précision sur les données et coude de la vraisemblance pour trouver le nombre de classes dans un mélange. Revue de Statistique Appliquée, 47(1), 47-62. PS



Technical papers

  • C. Biernacki (2004). Contribution à l'étude des mélanges de lois et à leurs applications. Mémoire d'Habilitation à Diriger des Recherches. PS

  • C. Biernacki (1997). Choix de modèles en classification. Ph.D. Thesis, Université de Technologie de Compiègne. PS

  • C. Biernacki & G. Govaert (1997). Using the Classification Likelihood to Choose the Number of Clusters. Computing Science and Statistics, 29(2), 451-457. PS



                                                                                                            Last update: 5th July 2016