Performance Comparison of K-Means and Expectation Maximization with Gaussian Mixture Models for Clustering

Final Report Presentation

Final Report PDF

I created this as a final project for a course at Missouri S&T called Statistical Decision Theory. I implemented the EM-GMM algorithm [1] in Matlab and compared the results with the built-in k-means function. EM-GMM is cool because it builds a generative model of the data, so you can use it for clustering if you make certain assumptions or you can use it to understand something about the data without actually looking at the data itself.

It was a challenging course because I don’t have a background in communications besides the introductory courses every EE takes, but we got some freedom for the final project that I really enjoyed!

[1] A. P. Dempster, N. M. Laird, and D. B. Rubin, \Maximum likelihood from incomplete data via the em algorithm,”Journal of the royal statistical society. Series B (methodological), pp. 1-38,1977.

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