References in zbMATH (referenced in 42 articles , 1 standard article )

Showing results 1 to 20 of 42.
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  1. García-Escudero, Luis A.; Mayo-Iscar, Agustín; Riani, Marco: Constrained parsimonious model-based clustering (2022)
  2. Greco, Luca: Robust fitting of mixtures of GLMs by weighted likelihood (2022)
  3. Brécheteau, Claire; Fischer, Aurélie; Levrard, Clément: Robust Bregman clustering (2021)
  4. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  5. Öner, Yüksel; Bulut, Hasan: A robust EM clustering approach: ROBEM (2021)
  6. Punzo, Antonio; Tortora, Cristina: Multiple scaled contaminated normal distribution and its application in clustering (2021)
  7. Brécheteau, Claire; Levrard, Clément: A (k)-points-based distance for robust geometric inference (2020)
  8. Cappozzo, Andrea; Greselin, Francesca; Murphy, Thomas Brendan: A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise (2020)
  9. García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
  10. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  11. Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
  12. Paindaveine, Davy; Remy, Julien; Verdebout, Thomas: Testing for principal component directions under weak identifiability (2020)
  13. Tavares, Ana Helena; Raymaekers, Jakob; Rousseeuw, Peter J.; Brito, Paula; Afreixo, Vera: Clustering genomic words in human DNA using peaks and trends of distributions (2020)
  14. Cerioli, Andrea; Farcomeni, Alessio; Riani, Marco: Wild adaptive trimming for robust estimation and cluster analysis (2019)
  15. del Barrio, E.; Cuesta-Albertos, J. A.; Matrán, C.; Mayo-Íscar, A.: Robust clustering tools based on optimal transportation (2019)
  16. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  17. Rivera-García, Diego; García-Escudero, Luis A.; Mayo-Iscar, Agustín; Ortega, Joaquín: Robust clustering for functional data based on trimming and constraints (2019)
  18. Torti, Francesca; Perrotta, Domenico; Riani, Marco; Cerioli, Andrea: Assessing trimming methodologies for clustering linear regression data (2019)
  19. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  20. Álvarez-Esteban, Pedro C.; Del Barrio, Eustasio; Cuesta-Albertos, Juan A.; Matrán, Carlos: Wide consensus aggregation in the Wasserstein space. Application to location-scatter families (2018)

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