R package 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 (2015) <https://arxiv.org/abs/1406.0808>, and Coretto and Hennig (2016) <https://arxiv.org/abs/1309.6895>.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
- Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
- Brodinová, Šárka; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian; Rohm, Maia: Robust and sparse (k)-means clustering for high-dimensional data (2019)
- Cerioli, Andrea; Farcomeni, Alessio; Riani, Marco: Wild adaptive trimming for robust estimation and cluster analysis (2019)
- Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
- Mazo, Gildas; Averyanov, Yaroslav: Constraining kernel estimators in semiparametric copula mixture models (2019)
- Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A reweighting approach to robust clustering (2018)
- Coretto, Pietro; Hennig, Christian: Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering (2017)
- Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
- McNicholas, Paul D.: Model-based clustering (2016)
- Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)