[EMAIL PROTECTED] wrote:
> I don't exactly understand what you do, could you show me the > program that you execute to do that?
I did such things sometimes ago, so the following is (as usual) without warranty. There are several methods, e.g. using Choleski factorization, singular value decomposition or principal components. Given "mdata" as original data matrix it works with hclust and should be applicable to kmeans too:
# with svd z <- svd(scale(mdata, scale=F))$u cl <- hclust(dist(z), method="ward")
# with princomp (rescaled) pc <- princomp(mdata, cor=FALSE) pcdata <- as.data.frame(scale(pc$scores)) cl <- hclust(dist(pcdata), method="ward")
... but as I mentioned, this is only an example, that methods working with the Euclidean distance can be applied to other distance measures, when an appropriate transformation of the data exist and, according to Gavin, there are indeed some other possibilities.
Thomas P.
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