Two methods are implemented to cluster data with finite mixture regression models. Those procedures deal with high-dimensional covariates and responses through a variable selection procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure. A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected using a model selection criterion (slope heuristic, BIC or AIC). Details of the procedure are provided in "Model-based clustering for high-dimensional data. Application to functional data" by Emilie Devijver (2016) <arXiv:1409.1333v2>, published in Advances in Data Analysis and Clustering.
|Author||Benjamin Auder <firstname.lastname@example.org> [aut,cre], Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut], Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]|
|Maintainer||Benjamin Auder <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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