This is not a well defined question, until your notions of "small" and
"nearest" are defined. In your specific example
rect.hclust(hc, k = 3, border = 2:5)
... will do what you are asking for. This is not likely to work in the general
case - imagine that your cluster of size two only meets the others at the root:
in that case you would be distorting the result significantly if you were to
merge it in with another cluster, simply based on membership size. That said,
perhaps the package dynamicTreeCut will help you find cuts in a dendrogram that
more closely match your intuition.
B.
On Mar 16, 2016, at 11:38 AM, Sheila the angel <from.d.putto at gmail.com>
wrote:
> In R, I have cut a dendrogram into clusters. However some of the clusters
> have only few samples. How can I merge the small clusters with nearest big
> cuter.
>
> hc <- hclust(dist(USArrests))
> plot(hc, cex = 0.6)
> rect.hclust(hc, k = 4, border = 2:5)
>
> It gives one cluster with only 2 samples. How can I merge it with nearest
> cluster?
>
> Thanks
> S.
>
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>
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