Title :
Identifying interesting visitors through Web log classification
Author :
Yu, Jeffrey Xu ; Ou, Yuming ; Zhang, Chengqi ; Zhang, Shichao
Author_Institution :
Chinese Univ. of Hong Kong, Shatin, China
Abstract :
Web site owners have trouble identifying customer purchasing patterns from their Web logs because the two aren´t directly related. Thus, organizations must understand their customers´ behavior, preferences, and future needs. This imperative leads many companies to develop a great many e-service systems for data collection and analysis. Web mining is a popular technique for analyzing visitor activities in e-service systems. It mainly includes Web text mining, Web structure mining and Web log mining. Our Web log mining approach classifies a particular site´s visitors into different groups on the basis of their purchase interest.
Keywords :
Web sites; consumer behaviour; customer services; data analysis; data mining; electronic commerce; pattern classification; purchasing; Web log classification; Web log mining; Web site; Web structure mining; Web text mining; customer behavior; customer purchasing patterns; data analysis; e-service systems; Data analysis; Graphics; Internet; Navigation; Robots; Search engines; Training data; Uniform resource locators; Web mining; Web server; Web mining; customer retention; data preparation; recommendation system; web log analysis;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2005.47
