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4.1.2 Joining (PNN+GLA)
The module \join" has an implementation of the PNN + GLA -A, RPNN
+ GLA [17] and SC-join -J algorithms. The SC-join algorithm does not
include GLA. The PNN + GLA and RPNN + GLA algorithms use GLA
after the distance has dropped below the threshold value set by switch -T.
The program continues joining classes until there is no decrease in the value
of the stochastic complexity. With big datasets which actually have a small
number of classes, this algorithm is slow, but is known to give good results
on certain type of datasets.
command: joingla
switches
-q quiet mode (no screen output)
-A use deterministic version (PNN instead of RPNN)
-EFF set epsilon to FF
-TFF GLA starting threshold
-JXX Join with SC criterion until XX classes remain
inputs
.data
data set
.partition1 input partition if -J set
.header
format le
outputs
.output
log le
.partition best partition (classication) found
.partition2 best partition (classication) found, if -J set
4.1.3 Splitting
The \split" module is an implementation of the Split+GLA [17] algorithm.
It works just the opposite to the joining method. There are both randomized
and deterministic versions -A of the algorithm. Splitting is continued until
there is no decrease in the value of SC in a prescribed number of steps -S. The
split method yields results very quickly and thus can be used to pre-screen
a dataset before the use of the automatic SC minimizer in the classication
module.
command: splitgla
switches
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