Discovering characteristic individual accessing behaviors in web environment

Authors:
Long Wang;Christoph Meinel;Chunnian Liu
Affiliations:
,Computer Science Department, Trier University, Trier, Germany;Hasso Plattner Institut, Potsdam University, Potsdam, Germany;Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing, China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

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Abstract

Discovering diverse individual accessing behaviors in web environment is required before mining the valuable patterns from behaviors of groups of visitors. In this paper, we investigate the data preparation in web usage mining, and especially focus on discovering characteristic individual accessing behaviors and give a systematic and formalized study on this topic. Based on the target usage patterns, individual user behavior through the web site can be discovered into five different categories: granular accessing behavior, linear sequential behavior, tree structure behavior, acyclic routing behavior and cyclic routing behavior. We also give different algorithms for discovering different kinds of behaviors. The experimental studies show that our discovery of individual behavior is very useful and necessary in web usage mining.