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.
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Yu, H., Luo, H., Chu, S. (2010). Web Users Access Paths Clustering Based on Possibilistic and Fuzzy Sets Theory. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_2
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DOI: https://doi.org/10.1007/978-3-642-17316-5_2
Publisher Name: Springer, Berlin, Heidelberg
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