I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments.

In the past, I've done it using a variety of techniques- hierarchical methods, EM etc. but the dataset has been much smaller with perhaps 12-15 variables.

I've used dimensionality reduction as a starting point and that has helped with smaller number of variables but with over a 100 variables, I'm a little befuddled. The dataset includes mostly numerical but also some categorical data.

How would I go about such an exercise? Distance measures in higher dimensions are tricky and so I'm seeking some guidance here.

A word about the tools of choice- I would like to run it in R but it'll most likely murder my laptop. Any specific database you guys could recommend?

Thanks so much everyone! Appreciate it.


You could try the LowRankModels package in julia. It's something of a generalized PCA approach. It supports boolean and cardinal data types as well as real numbers, so it has that going for it as well.

  • $\begingroup$ Thanks man! Any other packages you know of that might be helpful? $\endgroup$ – Nepze Tyson Jan 22 '15 at 17:57
  • $\begingroup$ That's the only one I've seen with boolean and cardinal data support. I'm sure that R has some support for PCA type analysis, but I'm not sure how to handle the categorical data with standard PCA. $\endgroup$ – user1348 Jan 22 '15 at 19:32

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