Description Usage Arguments Details Value Author(s) References See Also Examples
The EM-like algorithm for model-based clustering of finite mixture Gaussian models with unstructured dispersions.
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PARAM.org |
an original set of parameters generated
by |
A global variable called X.spmd
should exist in the
.pmclustEnv
environment, usually the working environment. The X.spmd
is the data matrix to be clustered, and this matrix has a dimension
N.spmd
by p
.
A PARAM.org
will be a local variable inside all EM-linke functions
em.step
, aecm.step
,
apecm.step
, apecma.step
, and
kmeans.step
,
This variable is a list containing all parameters related to models.
This function also updates in the parameters by the EM-like algorithms, and
return the convergent results. The details of list elements are initially
generated by set.global
.
A convergent results will be returned the other list variable
containing all new parameters which represent the components of models.
See the help page of PARAM
or PARAM.org
for details.
Wei-Chen Chen wccsnow@gmail.com and George Ostrouchov.
Programming with Big Data in R Website: https://pbdr.org/
Chen, W.-C. and Maitra, R. (2011) “Model-based clustering of regression time series data via APECM – an AECM algorithm sung to an even faster beat”, Statistical Analysis and Data Mining, 4, 567-578.
Chen, W.-C., Ostrouchov, G., Pugmire, D., Prabhat, M., and Wehner, M. (2013) “A Parallel EM Algorithm for Model-Based Clustering with Application to Explore Large Spatio-Temporal Data”, Technometrics, (revision).
Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977) “Maximum Likelihood from Incomplete Data via the EM Algorithm”, Journal of the Royal Statistical Society Series B, 39, 1-38.
Lloyd., S. P. (1982) “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 28, 129-137.
Meng, X.-L. and Van Dyk, D. (1997) “The EM Algorithm.an Old Folk-song Sung to a Fast New Tune”, Journal of the Royal Statistical Society Series B, 59, 511-567.
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# Save code in a file "demo.r" and run in 4 processors by
# > mpiexec -np 4 Rscript demo.r
### Setup environment.
library(pmclust, quiet = TRUE)
comm.set.seed(123)
### Generate an example data.
N.allspmds <- rep(5000, comm.size())
N.spmd <- 5000
N.K.spmd <- c(2000, 3000)
N <- 5000 * comm.size()
p <- 2
K <- 2
data.spmd <- generate.basic(N.allspmds, N.spmd, N.K.spmd, N, p, K)
X.spmd <- data.spmd$X.spmd
### Run clustering.
PARAM.org <- set.global(K = K) # Set global storages.
# PARAM.org <- initial.em(PARAM.org) # One initial.
PARAM.org <- initial.RndEM(PARAM.org) # Ten initials by default.
PARAM.new <- apecma.step(PARAM.org) # Run APECMa.
em.update.class() # Get classification.
### Get results.
N.CLASS <- get.N.CLASS(K)
comm.cat("# of class:", N.CLASS, "\n")
### Quit.
finalize()
## End(Not run)
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