I'll definitely look into the Brown clustering. I used this one with the hope of being able to eventually run spectral clustering on this (main problem: the KLI

I'll definitely look into the Brown clustering. I used this one with the hope of being able to eventually run spectral clustering on this (main problem: the KLIC defines a pre-metric, not a proper metric, so the order of operations is crucial). How does the Brown clustering method guarantee less sensitivity to the seeding words? Your visualization is interesting, I'd like to able to navigate it in order overcome most of the clutter. Originally I thought of mapping the clusters in 3d "clouds", but I think the dataset is too large for making a dimension-based visualization more than recreational - I mean, I'd probably be happier to read a cluster as a list.