A clustering algorithm based on latent semantic model

In order to precisely procure the Chinese person information on the web, especially distinguish from the namesake, this paper propose a clustering algorithm based on latent semantic model. It establishes for every document a latent semantic model of sentence-word matrix based on central distance, central segment, document length, etc, by building the central word library of person attributes. It clusters the similar documents by means of dynamic-extending clustering algorithm. Experiments prove that the algorithm gives high accuracy to documents clustering as well as maintaining the coherence of the person´s semantic information and highlighting the importance of semantic information under different sequences.