otrimle

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>.


References in zbMATH (referenced in 15 articles )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Cappozzo, Andrea; Greselin, Francesca; Murphy, Thomas Brendan: Anomaly and novelty detection for robust semi-supervised learning (2020)
  2. Farcomeni, Alessio; Punzo, Antonio: Robust model-based clustering with mild and gross outliers (2020)
  3. García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
  4. Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
  5. Brodinová, Šárka; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian; Rohm, Maia: Robust and sparse (k)-means clustering for high-dimensional data (2019)
  6. Cerioli, Andrea; Farcomeni, Alessio; Riani, Marco: Wild adaptive trimming for robust estimation and cluster analysis (2019)
  7. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  8. Mazo, Gildas; Averyanov, Yaroslav: Constraining kernel estimators in semiparametric copula mixture models (2019)
  9. Atkinson, Anthony C.; Riani, Marco; Cerioli, Andrea: Cluster detection and clustering with random start forward searches (2018)
  10. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A reweighting approach to robust clustering (2018)
  11. Punzo, Antonio; Mazza, Angelo; Maruotti, Antonello: Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions (2018)
  12. Coretto, Pietro; Hennig, Christian: Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering (2017)
  13. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
  14. McNicholas, Paul D.: Model-based clustering (2016)
  15. Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)