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cran
otrimle: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) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.
Maintained by Pietro Coretto. Last updated 4 years ago.
1 stars 1.48 score 1 dependents