Schema Evolution publication categorizer

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step forWeb search engines, our systemis not based on phrase analysis inside snippets, but instead uses latent semantic indexing on thewhole document content.Amain contribution of the paper is a novel strategy – called dynamic SVDclustering – to discover the optimal number of singular values to be used for clustering purposes. Moreover, the algorithm is such that the SVD computation step has in practice good performance, which makes it feasible to perform clustering when term vectors are available.We show that the algorithm has very good classification performance, and that it can be effectively used to cluster results of a search engine to make them easier to browse by users. The algorithm has being integrated into the Noodles search engine, a tool for searching and clustering Web and desktop documents.