mclust: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation , Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.

References in zbMATH (referenced in 117 articles , 1 standard article )

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  1. Athreya, A.; Priebe, C.E.; Tang, M.; Lyzinski, V.; Marchette, D.J.; Sussman, D.L.: A limit theorem for scaled eigenvectors of random dot product graphs (2016)
  2. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  3. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Feature selection for functional data (2016)
  4. Hasnat, Md.Abul; Alata, Olivier; Trémeau, Alain: Model-based hierarchical clustering with Bregman divergences and fishers mixture model: application to depth image analysis (2016)
  5. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  6. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  7. Bertoletti, Marco; Friel, Nial; Rastelli, Riccardo: Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion (2015)
  8. Bouveyron, Charles; C^ome, Etienne; Jacques, Julien: The discriminative functional mixture model for a comparative analysis of bike sharing systems (2015)
  9. Evans, Katie; Love, Tanzy; Thurston, Sally W.: Outlier identification in model-based cluster analysis (2015)
  10. Hennig, Christian; Lin, Chien-Ju: Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters (2015)
  11. Polonio, Luca; Di Guida, Sibilla; Coricelli, Giorgio: Strategic sophistication and attention in games: an eye-tracking study (2015)
  12. Strobl, Carolin; Kopf, Julia; Zeileis, Achim: Rasch trees: a new method for detecting differential item functioning in the Rasch model (2015)
  13. Vanbinst, Kiran; Ceulemans, Eva; Ghesquière, Pol; De Smedt, Bert: Profiles of children’s arithmetic fact development: A model-based clustering approach (2015)
  14. Zhang, Zhengwu; Pati, Debdeep; Srivastava, Anuj: Bayesian clustering of shapes of curves (2015)
  15. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  16. Biernacki, Christophe; Lourme, Alexandre: Stable and visualizable Gaussian parsimonious clustering models (2014)
  17. Browne, Ryan P.; McNicholas, Paul D.: Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models (2014)
  18. Guerra, Luis; Bielza, Concha; Robles, Víctor; Larrañaga, Pedro: Semi-supervised projected model-based clustering (2014)
  19. Menardi, Giovanna; Azzalini, Adelchi: An advancement in clustering via nonparametric density estimation (2014)
  20. Sabo, Miroslav: Consensus clustering with differential evolution (2014)

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