Research of a User Clustering Algorithm Based on the PLSA Model

With the rapid increase of web pages on the Internet,we can improve the efficiency of information searching and personalized services by performing a clustering analysis of the browsed records.Based on the information theory,the local weight and global weight are considered in the calculation of the weights in the session-page matrix.Based on the probabilistic latent semantic analysis,the conditional probability of the latent variable Z to page P is transformed into the conditional probability of the latent variable Z to session S.And then the transformed results are used in similarity calculation.The k-medoids algorithm is adopted to further improve the clursting results.Experimental results verify the validity and limitation of this algorithm.