Abstract
Compared to the regular documents, the major distinguishing characteristics of the Web documents are the dynamic hyper-structure. Thus, in addition to terms or keywords for regular document clustering, Web document clustering can incorporate some dynamic information such as the hyperlinks and the access patterns extracted from the user query logs. In this paper, we extend the concept of document clustering into Web document clustering by introducing the strategy of affinity-based similarity measure, which utilizes the user access patterns in determining the similarities among Web documents via a probabilistic model. Several comparison experiments are conducted using a real data set and the experimental results demonstrate that the proposed similarity measure outperforms the cosine coefficient and the Euclidean distance method under different document clustering algorithms.