Prof.
Christophe Biernacki
(Université Lille 1/INRIA)
22/06/2018 14:00
The "Big Data'' paradigm involves large and complex data sets where the clustering task plays a central role for data exploration. For this purpose, model-based clustering has demonstrated many theoretical and practical successes in a various number of fields. In this context, user-friendly software are essential for speeding up diffusion of such academic advance inside the applicative world....
Dr
Serge Iovleff
(CNRS / Laboratoire Paul Painlevé)
22/06/2018 14:45
The basic idea of Latent Block Model (LBM) consists in making permutations of individuals (rows) and variables (columns) in order to draw a correspondence structure between individuals and variables. The R package "blockcluster" implements generative LBMs for binary, contingency, continuous and categorical data sets.
In order to estimate the parameters, it implements BEM, BCEM algorithms. The...
Dr
Mohamed Sedki
(Université Paris-Sud)
22/06/2018 15:45
Les méthodes de clustering ne sont pas en reste quand il s'agît de regrouper des données de grande dimension.
L'échec dû à la grande dimension a incité la communauté des statisticiens à développer des procédures de sélection
de variables contenant l'information discriminante. Une grande partie de ces techniques sont mises à disposition
sous forme de packages R. Cette présentation est...
Dr
Andréa Rau
(INRA Jouy-en-Josas)
22/06/2018 16:15
In this talk, I will present some of the features of the R/Bioconductor package coseq, which provides a straightforward wrapper to identify groups of co-expressed genes from RNA sequencing data using Poisson mixture models (Rau et al., 2015), Gaussian mixture models (Rau et al., 2017), or the K-means algorithm (Godichon-Baggioni et al., 2018) in conjunction with appropriately chosen data...