Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. The code employs 'pbdMPI' to perform a expectation-gathering-maximization algorithm for finite mixture Gaussian models. The unstructured dispersion matrices are assumed in the Gaussian models. The implementation is default in the single program multiple data programming model. The code can be executed through 'pbdMPI' and MPI' implementations such as 'OpenMPI' and 'MPICH'. See the High Performance Statistical Computing website < https://snoweye.github.io/hpsc/> for more information, documents and examples.


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install.packages("pmclust")

0.2-0 by Wei-Chen Chen, a year ago


http://r-pbd.org/


Report a bug at http://group.r-pbd.org/


Browse source code at https://github.com/cran/pmclust


Authors: Wei-Chen Chen [aut, cre] , George Ostrouchov [aut]


Documentation:   PDF Manual  


Task views: Cluster Analysis & Finite Mixture Models, High-Performance and Parallel Computing with R


GPL (>= 2) license


Imports methods, MASS

Depends on pbdMPI, pbdBASE, pbdDMAT

Enhances MixSim


Enhanced by pbdDEMO.


See at CRAN