1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
2. School of Computer Science & Engineering, South China University of Technology, Guangzhou 510641, China
This paper proposes a new multi-granularity collection method for user behavior data which collects data through configurable server plug-in. The experiment results prove that the method can enhance quantity of Web usage mining data, simplify data cleaning and give multi-granularity information for the following mining,and provide high quality data for Web user behavior analysis.
赵洁, 董振宁, 张沙清, 肖南峰. 一种多粒度Web使用数据收集方法[J]. 现代图书情报技术, 2011, 27(2): 42-47.
Zhao Jie, Dong Zhenning, Zhang Shaqing, Xiao Nanfeng. A Collection Method for Multi-granularity Web Usage Data. New Technology of Library and Information Service, 2011, 27(2): 42-47.
[1] Srivastava J,Cooley R, Deshpande M, et al.Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data [J].SIGKDD Explorations, 2000,1(2):12-23.
[2] Barth M, Skubacz M, Stolz C. Web Performance Indicator by Implicit User Feedback - Application and Formal Approach . In: Proceedings of the 6th International Conference on Web Information Systems Engineering, Web Information Systems Engineering – WISE 2005. Berlin: Springer, 2005:689-700.
[3] Cooley R, Mobasher B, Srivastava J. Data Preparation for Mining World Wide Web Browsing Patterns [J].Journal of Knowledge and Information Systems,1999,1(1):5-32.
[7] Araya S, Silva M, Weber R. A Methodology for Web Usage Mining and Its Application to Target Group Identification [J]. Fuzzy Sets and Systems, 2004,148(1):139-152.
[8] Tao Y H, Hong T P, Su Y M.Web Usage Mining with Intentional Browsing Data [J]. Expert Systems with Applications: An International Journal, 2008,34(3): 1893-1904.
[9] Tao Y H, Hong T P, Lin W H. A Practical Extension of Web Usage Mining with Intentional Browsing Data Toward Usage [J]. Expert Systems with Applications: An International Journal,2009,36(2):3937-3945.
[10] Kim H K, Lee R Y. Frameworks for Web Usage Mining . //Studies in Computational Intelligence [M]. Berlin,Heidelberg: Springer,2009:121-134.
[12] Liu H B, Keelj V. Combined Mining of Web Server Logs and Web Contents for Classifying User Navigation Patterns and Predicting Users’ Future Requests [J]. Data & Knowledge Engineering, 2007,61(2):304-330.
[13] Wang X, Li B W. Intelligent Knowledge Recommendation System Based on Web Log and Cache Data [J]. Lecture Notes in Computer Science, 2006,4181:48-56.
[14] Inuzuka N, Hayakawa J I. A Unified Approach to Web Usage Mining Based on Frequent Sequence Mining . In: Proceedings of KES’07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks. Heidelberg,Berlin: Springer-Verlag,2007:987-994.
[15] Perugini S,Ramakrishnan N. Mining Web Functional Dependencies for Flexible Information Access [J]. Journal of the American Society for Information Science and Technology,2007,58(12):1805-1819.
