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pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model
Version 0.1-9
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 independent to most MPI applications. See the High Performance Statistical Computing website for more information, documents and examples.
Getting started
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Package details |
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| Author | Wei-Chen Chen [aut, cre], George Ostrouchov [aut] |
| Date of publication | 2016-12-19 08:34:03 |
| Maintainer | Wei-Chen Chen <[email protected]> |
| License | GPL (>= 2) |
| Version | 0.1-9 |
| URL | http://r-pbd.org/ |
| Package repository | View on CRAN |
| Installation |
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