I am trying to perform document-level clustering. I constructed the term-document frequency matrix and I am trying to cluster these high dimensional vectors using k-means. Instead of directly clustering, what I did was to first apply LSA's (Latent Semantic Analysis) singular vector decomposition to obtain the U,S,Vt matrices, selected a suitable threshold using the scree plot and applied clustering on the reduced matrices (specifically Vt because it gives me a concept-document information) which seemed to be giving me good results.
I've heard some people say SVD (singular vector decomposition) is clustering (by using cosine similarity measure etc.) and was not sure if I could apply k-means on the output of SVD. I thought it was logically correct because SVD is a dimensionality reduction technique, gives me a bunch of new vectors. k-means, on the other hand, will take the number of clusters as the input and divide these vectors into the specified number of clusters. Is this procedure flawed or are there ways in which this can be improved? Any suggestions?