person("Florent", "Langrognet", role = "aut", email = "florent.langrognet@univ-fcomte.fr"),
person("Remi", "Lebret", role = "aut", email = "remi@lebret.ch"),
person("Christian", "Poli", role = "aut", email = "christian.poli@inria.fr"),
person("Serge", "Iovleff", role = "aut", email = "serge.iovleff@univ-lille1.fr"),
person("Benjamin", "Auder", role = "aut", email = "benjamin.auder@u-psud.fr"),
person("Parmeet", "Bhatia", role = "ctb", email = "parmeet.bhatia@inria.fr"),
person("Anwuli", "Echenim", role = "ctb", email = "anwuli.echenim@inria.fr"),
person("Christophe", "Biernacki", role = "ctb", email = "christophe.biernacki@math.univ-lille1.fr"),
person("Gilles", "Celeux", role = "ctb", email = "gilles.celeux@math.u-psud.fr"),
person("Gerard", "Govaert", role = "ctb", email = "gerard.govaert@utc.fr"),
person("Quentin", "Grimonprez", role = "cre", email = "quentin.grimonprez@inria.fr"))
Packaged: 2020-05-29 15:40:42 UTC; grimonprez

Packaged: 2020-06-12 07:27:27 UTC; grimonprez

person("Quentin", "Grimonprez", role = "cre", email = "quentingrim@yahoo.fr"))

semi-supervised classification with mixture modelling <doi: 10.18637/jss.v067.i06>.

Title: Combining Mixture Components for Clustering
Description: The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.