Thank you very much for your help. everything works great
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Well, you could use the group membership from the clustering along
with, for instance, aggregate() to get the mean values for each
cluster, and pass those to kmeans() using the centers argument as
described in the help file.
Unless you want medoids instead of centroids, since you didn't specify?
This should get you started
> set.seed(42)
> x <- matrix(rnorm(200, 25, 5), 40, 5)
> x.clus <- hclust(dist(x))
> x.g4 <- cutree(x.clus, 4)
> x.cent <- aggregate(x, list(x.g4), mean)
> x.km <- kmeans(x, x.cent[,-1])
> xtabs(~x.g4+x.km$cluster)
x.km$cluster
x.g4 1 2 3 4
1 10 0 1 0
2
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