Robust Model-Based Clustering

Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) , and Coretto and Hennig (2017) <>.


Reference manual

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1.3 by Pietro Coretto, a year ago

Browse source code at

Authors: Pietro Coretto [aut, cre] , Christian Hennig [aut]

Documentation:   PDF Manual  

Task views: Robust Statistical Methods

GPL (>= 2) license

Imports stats, utils, graphics, grDevices, mclust, parallel, foreach, doParallel

See at CRAN