Web Users Access Paths Clustering Based on Possibilistic and Fuzzy Sets Theory
Web users access paths clustering is important to conduct Web page prediction. In this paper, a novel Web users access paths clustering method is proposed based on possibilistic and fuzzy sets theory. Firstly, a similarity measure method of access paths is proposed based on differences between paths factors, such as the length of time spent on visiting a page, the frequency of a page accessed and the order of pages accessed. Furthermore, considering that clusters tend to have vague or imprecise boundaries in the path clustering, a novel uncertain clustering method is proposed based on combining advantages of fuzzy clustering and possibility clustering. A λ_cut set is defined here to process the overlapping clusters adaptively. The comparison of experimental results shows that our proposed method is valid and efficient.
Web mining access path clustering possibilistic theory fuzzy sets theory overlapping
Hong Yu Hu Luo Shuangshuang Chu
Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 P.R. China
国际会议
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)
重庆
英文
12-23
2010-11-19(万方平台首次上网日期,不代表论文的发表时间)