I would love to see more books/articles/blogs on unsupervised learning and ensemble techniques. E.G. - can I use k-means clustering as input to train a naïve Bayes classifier?
I'm actually doing some research on that example now. The problem I've run into is that you need to use parameters that are relevant to making classifications in the clustering algorithm. This is unfortunately kind of a chicken/egg problem, because removing parameters from the clustering algorithm changes the clusters.
Interesting...I should write a blog post about some techniques I've used that are similar to your example (modulo specific algorithms). Is there something specific you are trying to accomplish?
I would read it! I work with a specific kind of high(er) dimensional medical imaging data, and I think unsupervised learning could be used for classification and foreground separation. K-means is giving me some promising preliminary results, but I'd like to assign samples continuous probabilities rather than binary classifications. I'm relatively new to ML but trying to incorporate it into my research, so I apologize if any of that doesn't make sense!