A top down algorithm for mining web access patterns from web logs

Authors:
Guo Jian-Kui;Ruan Bei-jun;Cheng Zun-ping;Su Fang-zhong;Wang Ya-qin;Deng Xu-bin;Shang Ning;Zhu Yang-Yong
Affiliations:
Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, FuZhou University, Fuzhou, China;Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Fudan University, Shanghai, China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

This paper proposes a new algorithm, called TAM WAP(the shorthand forTop down Algorithm for Mining Web AccessPatterns), to mine interesting WAP from Web logs. TAM WAP searches the P tree database in the top down manner to mine WAP. By selectively building intermediate data according to the features of current area to be mined, it can avoid stubbornly building intermediate data for each step of mining process. The experiments for both real data and artificial data show that our algorithm outperforms conventional methods.