cluster

Dear Weiwei,

your question sounds a bit too general and complicated for the R-list.
Perhaps you should look for personal statistical advice.
The quality of methods (and especially distance choice) for down-sampling
ceratinly depends on the structure of the data set. I do not see at the moment why
you need any down-sampling at all, and you should find out first if and
why it's a good thing to do (by whatever method).

An obvious candidate for a clustering algorithm would be pam/clara in
package cluster, because this approach chooses points already in the data
set as cluster centroids (and produces therefore a proper subsample),
which does not apply to most other clustering methods.

However, in
 C. Hennig and L. J. Latecki:  The choice of vantage objects for image
retrieval.  Pattern Recognition 36 (2003), 2187-2196.
the clustering approach has been clearly outperformed by some stepwise
selection approaches for down-sampling - admittedly in a different kind of
problem, but I think that the reasons for this may apply also to your
situation,

You can compare different clusterings (or choices of a subset) by
cross-validation or
bootstrap applied to the resulting decision tree in the classification
problem.

Best,
Christian

cluster

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