Abstract: A fast hybrid clustering algorithm for Web documents clustering is proposed which optimizes the initial center values of K-means algorithm through STC algorithm.Firstly，the initial center values are extracted after the Web document set is clustered by STC algorithm.Secondly，by mapping the each internal node of suffix tree into M-dimensional VSM，each feature term weights is computed using TF-IDF extended with phrases.Finally，the final result is generated by K-means algorithm.The evaluation experiments indicate that the new hybrid algorithm is more effective on clustering documents than ordinary K-means and STC algorithm.Moreover，it is as fast as K-means and STC algorithm.