Hi,
I have a very large dataset of 3008 individuals and 800 numerical variables.
In fact it is a table of 3008 36-monthes multivariated time series that I
would like to classify with an unsupervised algorithm
I had a look at the function kkmeans of e1071 package, which seems to be a
kernel weighted version of the algorithm algorithm, and the bclust from the
same package which does bootstrapping of kmean algorithm (bagging)
I had the idea of combining these two functions, in fact using kkmeans in
bclust parameters.
What should I do ? Reducing dimension and then classify, or classifying only
?
Should I use kkmeans only ? or bclust only ?
I'm very interested in your opinions.
Laurent.
--
«l'homme chéri ne dure plus d'un jour»
«Moi» @<http://www.le-valdo.com>
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