c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("George", "Ostrouchov", role = "aut"))
R (>= 3.0.0), methods, rlecuyer, pbdMPI (>= 0.2-2), MASS
The pmclust aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The package employs Rmpi to perform a
expectation-gathering-maximization (EGM) 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 (SPMD) programming model. The code can be executed through Rmpi and
independent to most MPI applications. See the High Performance
Statistical Computing (HPSC) website for more information, documents
and examples.
Wei-Chen Chen [aut, cre],
George Ostrouchov [aut]

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 independent to most MPI applications.
See the High Performance
Statistical Computing website for more information, documents
and examples.

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.