Web users access paths clustering based on possibilistic and fuzzy sets theory

Authors:
Hong Yu;Hu Luo;Shuangshuang Chu
Affiliations:
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

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.