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I recently read a fascinating article describing methods for clustering data without assuming a fixed number of clusters.

The article even includes some sample code, in a mix of Ruby, Python, and R. However, the meat of the analysis is performed using scikit-learn's Dirichlet Process Gaussian Mixture Model to actually find clusters in some sample data taken from McDonald's menu.

Obviously, this a a great excuse to learn some more python, but I'm lazy and would like to find a ready-made R package that can take a dataframe and return clusters, in a manner similar to the kmeans function. A quick search on CRAN reveals the packages dpmixsim and profdpm. Any suggestions for the best place to start?

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Also cran.r-project.org/web/packages/DPpackage which has Dirichlet process clustering I think, and cran.r-project.org/web/packages/bayesm has some DP stuff, don't remember what. – scellus May 16 '12 at 16:11
Whoever voted down- could you please leave a comment? – Zach May 16 '12 at 17:07
I am not the one who downvoted but I wanted to comment that one can simply add the number of clusters to the likelihood of the mixture distribution (often solved using E-M algorithm). It appears from your problem a Gaussian mixture model is assumed ( I don't know how the Dirichlet process fits in). There are some excellent books on mixture distributions. I recommend two by McLachlan. Finite Mixture Models (Wiley Series in Probability and Statistics) [Hardcover] Geoffrey McLachlan (Author), David Peel (Author) and – Michael Chernick May 16 '12 at 17:46
Mixture Models (Statistics: A Series of Textbooks and Monographs) [Hardcover] G. J. McLachlan (Author), K. E. Basford (Author) – Michael Chernick May 16 '12 at 17:47
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@Michael: maximum likelihood with a varying number of (gaussien) clusters — how do you make it not to overlearn, especially if the cluster width is a parameters as well? – scellus May 16 '12 at 18:17

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I looked at this more carefully, and the package bayesm has rDPGibbs which does "Density Estimation with Dirichlet Process Prior and Normal Base", a kind of Dirichlet clustering. DPpackage has DPdensity which looks similar. I haven't tried these packages myself, so I have no idea how well they work in practice. Details such as parameterization of the normal base and the possibility to set hyperpriors for the DP parameter may be significant.

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