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I am clustering a dataset using the pam command (from {cluster} package), and I wish to decide on the number of clusters to use.

I was able to implement The_Elbow_Method in R (see wiki) for doing that. But that doesn't provide me with any solid criteria (like AIC, for example) for decision.

I came by the {clValid} package which looks promising, but I wanted to know if there are any other R solutions (you know of) for choosing the number of clusters for pam?

Here's some dummy code if someone wants to show examples:

data(iris)
head(iris)
require(cluster)
pam(iris[,1:4], 3)
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  • $\begingroup$ I came across a package recently that is supposed to help with this and they reference using it with pam. I haven't tried it yet but it looks promising. Check out the vignette. cran.r-project.org/web/packages/clues/index.html $\endgroup$
    – Robert
    Nov 18, 2010 at 20:58

3 Answers 3

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You may find an answer to a similar question useful. I have also used clValid but, as I recall, it was rather slow (at least for relatively large datasets).

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    $\begingroup$ @Tal Galili You are welcome Tal! If you find out some other method for choosing the number of clusters, share it with us. $\endgroup$
    – gd047
    Sep 16, 2010 at 21:12
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The fpc package provides a few clustering statistics. If you're looking for information criteria in particular, the cluster.stats method provides an information based distance. For mixture models based on clustering, the BIC is available.

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  • $\begingroup$ fpc also has pamk, which can detect the number of clusters. $\endgroup$
    – Sam Brightman
    Dec 30, 2013 at 12:02
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What about silhouette?

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  • $\begingroup$ pam actually gives that out of the box (when using plot on the pam). I'm still open for other solutions :) $\endgroup$
    – Tal Galili
    Sep 16, 2010 at 15:53
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    $\begingroup$ @Tal I know, but you haven't mentioned it. $\endgroup$
    – user88
    Sep 16, 2010 at 16:07
  • $\begingroup$ True, I simply forgot: +1 :) $\endgroup$
    – Tal Galili
    Sep 16, 2010 at 19:20
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    $\begingroup$ Also I still think that this is a very good method. $\endgroup$
    – user88
    Sep 16, 2010 at 19:39

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