The Research Of Data Mining Based On Web Log

This thesis includes four parts in which the technologies of Web Log Mining are systematically researched. In the first part we summarize the techniques of Web Log Mining, and present the significance of the research on Web Log Mining, the status of research and the problem which Web Log Mining faces with. In the second part we research on data preparation which is the key process of Web Log Mining and analyze each task of data preparation in detail. In the third part analyze principles and general methods of clustering based Data Mining in pattern discovery phase, and introduce the theory of fuzzy clustering. In the fourth part, present a fuzzy clustering algorithm of Web users browsing pattern. The algorithm bases on user viewing time discretization that avoids only taking user browsing times or user browsing time into account. The algorithm adopts graphic theory to get fuzzy equivalence matrix from fuzzy similar matrix. The algorithm is proved to have better accuracy, fewer CPU time and better scalability than traditional methods by the experiments.