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pmclust (version 0.2-0)

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

Description

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.

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Version

Install

install.packages('pmclust')

Monthly Downloads

142

Version

0.2-0

License

GPL (>= 2)

Maintainer

Wei-Chen Chen

Last Published

February 2nd, 2018

Functions in pmclust (0.2-0)

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