R package clustMD: Model Based Clustering for Mixed Data. Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models.
Keywords for this software
References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Biernacki, Christophe; Marbac, Matthieu; Vandewalle, Vincent: Gaussian-based visualization of Gaussian and non-Gaussian-based clustering (2021)
- Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
- Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
- Selosse, Margot; Jacques, Julien; Biernacki, Christophe: Model-based co-clustering for mixed type data (2020)
- Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
- Fernández, Daniel; Arnold, Richard; Pledger, Shirley; Liu, Ivy; Costilla, Roy: Finite mixture biclustering of discrete type multivariate data (2019)
- Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- O’Hagan, Adrian; Ferrari, Colm: Model-based and nonparametric approaches to clustering for data compression in actuarial applications (2017)
- Tekumalla, Lavanya Sita; Rajan, Vaibhav; Bhattacharyya, Chiranjib: Vine copulas for mixed data: multi-view clustering for mixed data beyond meta-Gaussian dependencies (2017)
- McParland, Damien; Gormley, Isobel Claire: Model based clustering for mixed data: clustMD (2016)