These functions implement initialization of EM-like algorithms
for model-based clustering based on X.spmd, and
initialization of K-means algorithm by randomly picking samples
from data based on X.spmd.
*.dmat's are ddmatrix versions.
initial.RndEM(PARAM)
initial.em(PARAM, MU = NULL)
initial.center(PARAM, MU = NULL) initial.RndEM.dmat(PARAM)
initial.em.dmat(PARAM, MU = NULL)
initial.center.dmat(PARAM, MU = NULL)
an original set of parameters generated
by set.global.
a center matrix with dim = \(p \times K\).
The best initial starting points PARAM will be returned among
all random starting points. The number of random starting points is assigned
by set.global to a list variable CONTROL.
See the help page of initial.em and set.global
for details.
For initial.RndEM,
the procedure is implemented by randomly picking
.pmclustEnv$CONTROL$RndEM.iter starting points from data
X.spmd
and run one E-step to obtain the log likelihood.
Then pick the starting point with the highest log likelihood as the
best choice to pursue the MLEs in further EM iterations.
This function repeatedly run initial.em by
.pmclustEnv$CONTROL$RndEM.iter random starts and pick the
best initializations from the random starts.
For initial.em,
it takes X.spmd from the global environment and
randomly pick \(K\) of them as the centers of \(K\) groups.
If MU is specified, then this MU
will be the centers.
The default identity dispersion in PARAM$SIGMA will be used.
Then, one E-step will be called to obtain the log likelihood and new
classification will be updated.
This function is used to implement the RndEM procedure for more
elaborate initialization scheme in initial.RndEM.
Potentially, several random starts should be tried before running EM
algorithms. This can benefit in two aspects including:
shorter convergent iterations and better classification results.
For initial.center,
if MU is given, then the center will be assigned according.
Programming with Big Data in R Website: http://r-pbd.org/
Maitra, R. (2009) “Initializing partition-optimization algorithms”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6:1, 114-157.
set.global,
em.step,
aecm.step,
apecm.step,
apecma.step,
kmeans.step.
set.global.dmat,
kmeans.step.dmat.
# NOT RUN {
# Examples can be found in the help page of em.step(),
# aecm.step(), apecm.step(), apecma.step(), and kmeans.step().
# Examples for ddmatrix version can be found in the help pages of
# kmeans.step.dmat().
# }
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