Question to Y-hat folks: why cluster in 2D? Granted, clustering in 300D is hard :) Still, the 2D projection must add a significant metric distortion. Why not a middle ground, say, 5-10D ?
There's often no need to cluster in a higher space because the outcome is the same, and clustering is more expensive when there's more dimensions.
To my mind this isn't so bad -- the notion of overlapping clusters is something I think most people actually accept (think of clusters as tags rather than a partitioning and you can get the idea). However I can see why you may prefer to have your clusters present more clearly in 2D.
word2vec -> t-SNE(2D) -> HDBSCAN
by
word2vec -> t-SNE(10D) -> HDBSCAN -> t-SNE(2D) (or just 2D projection that separates well the centroids)
?
----
About clustering in 300D, specifically for word2vec vectors, to which cosine similarity is important: Random-Projections LSH is supposed to "be compatible" with the cosine similarty. I have been toying with it and getting sort of good recalls (70%) with 100D.
It might help a lot with regards to finding the nearest neighbours. Or were you meaning that the clustering itself will be meaningless, regardless of our ability to find neighbours?
i.e. it's nice for visualisation, and it removes noise. It would be interesting to see discussion from y-hat about where the sweet spot between lack of noise and still keeping relevant information is. I think because the subject matter is pretty simply to cluster, 2D works well enough and keeps everything simple.