Preprocessing: A Prerequisite for Discovering Patterns in Web Usage Mining Process
Web log data is usually diverse and voluminous. This data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discover-. Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful patterns. As in most data mining applications, data preprocessing involves removing and filtering redundant and irrelevant data, removing noise, transforming and resolving any inconsistencies. In this paper, a complete preprocessing methodology having merging, data cleaning* user/session identification and data formatting and summarization activities to improve the quality of data by reducing the quantity of data has been proposed. To validate the efficiency of the proposed preprocessing methodology, several experiments are conducted and the results show that the proposed methodology reduces the size of Web access log files down to 7382％ of the initial size and offers richer logs that are structured for further stages of Web Usage Mining (WUM). So preprocessing of raw data in this WUM process is the central theme of this paper.
Data Preprocessing Web log data Web usage mining User/Session identification
Ramya C Shreedhara K S Kavitha G
M.Tech (Final Year), Professor & Chairman and Lecturer, Dept. of Studies in CS&E U.B.D.T College of Engineering, Davangere Davangere University, Karnataka, INDIA
国际会议
海口
英文
317-321
2011-02-22（万方平台首次上网日期，不代表论文的发表时间）