Web mining based on Bayes latent semantic model
Gong Xiujun, Zhongzhi Shi · 2002
With the increase of information on Internet, web mining has been the focus of data mining. In this paper, we put forward a semi-supervised learning strategy consisting of two stages. First stage labels the documents that include latent class variables by using Bayes latent semantic model; at the second stage, based on the results from first stage, we label the documents excluding latent class variables with the naive Bayes models. Experimental results show that this algorithm has a good precision and recall rate.