bclust
R package bclust: Bayesian Hierarchical Clustering Using Spike and Slab Models. Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
Sorted by year (- Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Nia, Vahid Partovi; Ghannad-Rezaie, Mostafa: Agglomerative joint clustering of metabolic data with spike at zero: a Bayesian perspective (2016)
- Nia, Vahid Partovi; Davison, Anthony C.: A simple model-based approach to variable selection in classification and clustering (2015)
- Bouveyron, Charles; Brunet-Saumard, Camille: Model-based clustering of high-dimensional data: a review (2014)
- Vahid Nia; Anthony Davison: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust (2012) not zbMATH