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 (2015) <https://arxiv.org/abs/1406.0808>, and Coretto and Hennig (2016) <https://arxiv.org/abs/1309.6895>.
|Author||Pietro Coretto [aut, cre], Christian Hennig [aut]|
|Date of publication||2016-11-30 14:55:24|
|Maintainer||Pietro Coretto <email@example.com>|
|License||GPL (>= 2)|
banknote: Swiss Banknotes Data
InitClust: Robust Initilization for Model-based Clustering Methods
otrimle: Optimally Tuned Robust Improper Maximum Likelihood Clustering
plot.otrimle: Plot Methods for OTRIMLE Objects
plot.rimle: Plot Methods for RIMLE Objects
rimle: Robust Improper Maximum Likelihood Clustering
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