Research of Chinese spectral clustering with LSA
结合LSA的中文谱聚类算法研究

Traditional text samples similarity matrix for spectral cluster heavily rely on the vector space model which ignores the semantic relationship among terms. It will give rise to problems such as curse of dimensionality, feature redundancy and high computing cost. To solve the problems above, this paper proposed a new method based on LSA to solve it, which used SVD to lowering rank of matrices. The experimental results turn out that the new method enhances the cluster accuracy and less the data-process elapsed time.