I spent a few hours at the Future of AI symposium, at which various luminaries speculated about the future of machine learning. Mainly I learned that representatives of huge companies are willing to perform insane extrapolations of their current technologies, which are all, entirely, based on convolutional nets (and some recurrent nets) as Yann Lecun (NYU) reminded us. There was an interesting call for unsupervised methods: Clearly you don't have AI if all you can do is supervised learning!
In the afternoon, Dan Foreman-Mackey told me to use k-means. That in answer to my question: I am trying to find structure in 15-dimensional chemical-abundance space (the output of The Cannon); what algorithms should I use? His answer was k-means or else suck it up and do extreme deconvolution. So of course I will go with k-means in the short run! That said, extreme deconvolution is the right tool for this job eventually.