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I have a situation where I have a set of n data points and want to specify k values that best approximate the values in the set. (it's an image-color reduction problem)

MATLAB has a magic algorithm using something called minimum-variance quantization that will do this (although I can't find a description of how this actually works). I've also stumbled upon something called k-means clustering. What is the difference between these two or are they the same? Where might I be able to learn about these? I found some information describing k-means clustering, but I couldn't find anything on minimum-variance quantization.

Thank you!