There is a lot of content about how to cluster, say, customers (k-means, EM clustering, etc.).
However, is there a way to reverse cluster customers? Meaning, let's say I have 20 customers, and want to divide them into two groups, such that those two groups are as similar to each other as possible. Is there an algorithm for that?
Here is more information about my specific situation: Each of 20 customers has five features associated with him or her. These customers vary along those five dimensions. I'd like the means of two (or three, etc.) groups to be as close to each other as possible. I could try to solve the problem by brute force by trying all combinations of customers, and see which grouping is best, but I'm sure there's a better way to do it.