Do you mean replacing word2vec -> t-SNE(2D) -> HDBSCAN by word2vec -> t-SNE(10D) -> HDBSCAN -> t-SNE(2D) (or just 2D projection that separates well the centr

Do you mean replacing 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?