pmclust v0.2-0


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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 <> for more information, documents and examples.

Functions in pmclust

Name Description
One E-Step Compute One E-step and Log Likelihood Based on Current Parameters
generate.basic Generate Examples for Testing
Set of PARAM A Set of Parameters in Model-Based Clustering.
Internal Functions All Internal Functions
One Step of EM algorithm One EM Step for GBD
One M-Step Compute One M-Step Based on Current Posterior Probabilities
assign.N.sample Obtain a Set of Random Samples for X.spmd
Independent logL Independent Function for Log Likelihood
pmclust-package Parallel Model-Based Clustering
Update Class of EM or Kmenas Results Update CLASS.spmd Based on the Final Iteration
Set of CONTROL A Set of Controls in Model-Based Clustering.
mb.print Print Results of Model-Based Clustering
pmclust and pkmeans Parallel Model-Based Clustering and Parallel K-means Algorithm
print.object Functions for Printing or Summarizing Objects According to Classes
as functions Convert between X.gbd (X.spmd) and X.dmat
get.N.CLASS Obtain Total Elements for Every Clusters
Initialization Initialization for EM-like Algorithms
EM-like algorithms EM-like Steps for GBD
Read Me First Read Me First Function
Set Global Variables Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
generate.MixSim Generate MixSim Examples for Testing
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Last month downloads


Date 2018-02-01
LazyLoad yes
LazyData yes
License GPL (>= 2)
MailingList Please send questions and comments regarding pbdR to
NeedsCompilation yes
Packaged 2018-02-02 04:12:21 UTC; snoweye
Repository CRAN
Date/Publication 2018-02-02 04:41:01 UTC

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