K-Means vs. Minimum-Variance Quantization

AI Thread Summary
K-means clustering and minimum-variance quantization are both techniques used for approximating data points, particularly in applications like image color reduction. K-means clustering groups data into k clusters by minimizing the variance within each cluster, while minimum-variance quantization aims to minimize the overall quantization error when representing data with fewer values. The user is seeking more information on minimum-variance quantization, as resources on k-means are more readily available. Links to relevant literature and MATLAB documentation were provided for further exploration. Understanding the differences and applications of these methods can enhance data approximation strategies.
czechman45
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Hi,

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!
 
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