The research on fuzzy data mining applied on browser records

Authors:
Qingzhan Chen;Jianghong Han;Yungang Lai;Wenxiu He;Keji Mao
Affiliations:
School of Computer Science, Heifei University of Technology, Hefei, Anhui, China;School of Computer Science, Heifei University of Technology, Hefei, Anhui, China;College of Informatin Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;College of Informatin Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;College of Informatin Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

With the technological advances, the Internet has been an important part of everyday life. Governmental institutions and enterprises tend to advertise and market through the internet. With the travelling records of browsers, one can analyze the preference of web pages, further understand the demands of consumers, and promote the advertising and marketing. In this study, we use Maximum Forward Reference (MFR) algorithm to find the travel pattern of browsers from web logs. Simultaneously, experts are asked to evaluate the fuzzy importance weightings for different webs. Finally, we employ fuzzy data mining technique that combines apriori algorithm with fuzzy weights to determine the association rules. From the yielded association rules, one can be accurately aware of the information consumers need and which webs they prefer. This is important to governmental institutions and enterprises. Enterprises can find the commercial opportunities and improve the design of webs by means of this study. Governmental institutions can realize the needs of people from the obtained association rules, make the promotion of policy more efficiently, and provide better services.