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?