Mixture models : Theory and applications

Session

Softwares

22 juin 2018, 14:00

Amphi Risler (jeudi), Amphi Tisserand (vendredi) (Paris)

16. MASSICCC: A SaaS Platform for Clustering Mixed Data

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....

17. "Blockcluster" and "simerge" : Two R packages for Latent Block Models and Latent Block Models with co-variables implemented in C++

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...

24. Packages R pour la réduction de dimension en clustering

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...

25. Co-expression analyses of RNA-seq data in practice with the R/Bioconductor package coseq

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...