This is kind of a fun example, and you might find the fuzzy clustering technique useful, as I have, for exploratory data analysis. In this Gist, I use the unparalleled breakfast dataset from the smacof package, derive dissimilarities from breakfast item preference correlations, and use those dissimilarities to cluster foods.

Fuzzy clustering with fanny() is different from k-means and hierarchical clustering, in that it returns probabilities of membership for each observation in each cluster. Here, I ask for three clusters, so I can represent probabilities in RGB color space, and plot text in boxes with the help of this StackOverflow answer.

The colors and the MDS configuration highlight the three primary clusterings of breakfast items into what we’ll call a muffin group, a bread group, and a sweet group. Of course, cluster identification is a subjective exercise, made even more so by use of probabilistic membership, but I’m pretty happy with this breakfast analysis.