Variable selection in model-based clustering.
It is devoted to the variable selection in model-based clustering. It is a greedy algorithm associated to the SRUW modeling proposed by C.Maugis, G.Celeux and M.-L. Martin-Magniette in [1] and [2], modifying the method of Raftery and Dean [3] and improving our SelvarClust algorithm [4]. The SRUW modeling takes into account the three possible roles: relevant, redundant and independent variables.
This software allows to study datasets where observations are described by quantitative variables. It returns a data clustering and the selected model composed of the number of clusters, the mixture form, the variance matrix form for the linear regression and the independent Gaussian density, and the variable partition.
