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


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Documentation for package ‘pmclust’ version 0.1-7

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pmclust-package Parallel Model-Based Clustering
.PMC.CT A Set of Controls in Model-Based Clustering.
.pmclustEnv Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
aecm.step EM-like Steps for GBD
apecm.step EM-like Steps for GBD
apecma.step EM-like Steps for GBD
as.dmat Convert between X.gbd (X.spmd) and X.dmat
as.gbd Convert between X.gbd (X.spmd) and X.dmat
as.spmd Convert between X.gbd (X.spmd) and X.dmat
assign.N.sample Obtain a Set of Random Samples for X.spmd
CHECK Read Me First Function
CLASS.dmat Read Me First Function
CLASS.spmd Read Me First Function
COMM.RANK Read Me First Function
COMM.SIZE Read Me First Function
CONTROL A Set of Controls in Model-Based Clustering.
e.step Compute One E-step and Log Likelihood Based on Current Parameters
e.step.dmat Compute One E-step and Log Likelihood Based on Current Parameters
em.onestep One EM Step for GBD
em.onestep.dmat One EM Step for GBD
em.step EM-like Steps for GBD
em.step.dmat EM-like Steps for GBD
em.update.class Update CLASS.spmd Based on the Final Iteration
em.update.class.dmat Update CLASS.spmd Based on the Final Iteration
ETA A Set of Parameters in Model-Based Clustering.
generate.basic Generate Examples for Testing
generate.MixSim Generate MixSim Examples for Testing
get.CLASS Obtain Total Elements for Every Clusters
get.N.CLASS Obtain Total Elements for Every Clusters
get.N.CLASS.dmat Obtain Total Elements for Every Clusters
indep.logL Independent Function for Log Likelihood
indep.logL.dmat Independent Function for Log Likelihood
initial.center Initialization for EM-like Algorithms
initial.center.dmat Initialization for EM-like Algorithms
initial.em Initialization for EM-like Algorithms
initial.em.dmat Initialization for EM-like Algorithms
initial.RndEM Initialization for EM-like Algorithms
initial.RndEM.dmat Initialization for EM-like Algorithms
kmeans.step EM-like Steps for GBD
kmeans.step.dmat EM-like Steps for GBD
kmeans.update.class Update CLASS.spmd Based on the Final Iteration
kmeans.update.class.dmat Update CLASS.spmd Based on the Final Iteration
m.step Compute One M-Step Based on Current Posterior Probabilities
m.step.dmat Compute One M-Step Based on Current Posterior Probabilities
mb.print Print Results of Model-Based Clustering
MU A Set of Parameters in Model-Based Clustering.
p.times.logtwopi Read Me First Function
PARAM A Set of Parameters in Model-Based Clustering.
PARAM.org A Set of Parameters in Model-Based Clustering.
pkmeans Parallel Model-Based Clustering and Parallel K-means Algorithm
pmclust Parallel Model-Based Clustering and Parallel K-means Algorithm
print.pkmeans Functions for Printing or Summarizing Objects According to Classes
print.pmclust Functions for Printing or Summarizing Objects According to Classes
readme Read Me First Function
readme.dmat Read Me First Function
SAVE.iter Read Me First Function
SAVE.param Read Me First Function
set.global Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
set.global.dmat Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
set.global.gbd Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
SIGMA A Set of Parameters in Model-Based Clustering.
U.dmat Read Me First Function
U.spmd Read Me First Function
W.dmat Read Me First Function
W.dmat.rowSums Read Me First Function
W.spmd Read Me First Function
W.spmd.rowSums Read Me First Function
X.dmat Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
X.gbd Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
X.spmd Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat
Z.colSums Read Me First Function
Z.dmat Read Me First Function
Z.spmd Read Me First Function