The Research On Web-based Usage Mining

The Internet is a global, distributed, dynamic information warehouse. A mass of digital information is stored in it. Today it has become important resource of obtaining daily information. But, because of enormous information, it has become more and more difficult to discover useful information to every user. And it is also difficult to learn about rationality of organization structure of web site. But as an increasing number of users access information on the Web, there is a great opportunity to learn about inner structure, constitutes, content, access frequency from the server logs. And it is convenient to obtain the log files on the Web. So analyzing the web log is effective and feasible.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 will face with. In the second part we discuss three phases of Web Log Mining: Preprocessing, Pattern Discovery, Pattern Analysis. The third part analyze principles and general methods of clustering based Data Mining in Pattern Discovery phase, and introduce the application and research of fuzzy clustering theory. In the fourth part, we introduce the FCM arithmetic, and present a data structure and the corresponding arithmetic which suit to Web Log Mining. The data structure is a User_URL matrix. Mining arithmetic that uses fuzzy clustering, will discover similar access interest of web session group.