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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.

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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.

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  • $\begingroup$ 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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