k-means are actually useful for bootstrapping statistical mixtures when you don't have good priors.
Just for this convenient use alone I would place them above PCA.
Just for this convenient use alone I would place them above PCA.
Why not just bootstrap in the regular old non-parametric way? Why inject k-means into it?
Finding good ones can be very problematic. The k-means process is a reasonable method to get good enough starting values without having to think/compute too much.
https://en.wikipedia.org/wiki/Expectation%E2%80%93maximizati...
Personally I never would have guessed that, in retrospect it makes sense... to each their own!