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Knowledge and Information Systems

, Volume 4, Issue 2, pp 151–167 | Cite as

Correlation-Based Web Document Clustering for Adaptive Web Interface Design

  • Zhong Su
  • Qiang Yang
  • Hongjiang Zhang
  • Xiaowei Xu
  • Yu-Hen Hu
  • Shaoping Ma
Web Information Systems Engineering Paper

Abstract

A great challenge for web site designers is how to ensure users' easy access to important web pages efficiently. In this paper we present a clustering-based approach to address this problem. Our approach to this challenge is to perform efficient and effective correlation analysis based on web logs and construct clusters of web pages to reflect the co-visit behavior of web site users. We present a novel approach for adapting previous clustering algorithms that are designed for databases in the problem domain of web page clustering, and show that our new methods can generate high-quality clusters for very large web logs when previous methods fail. Based on the high-quality clustering results, we then apply the data-mined clustering knowledge to the problem of adapting web interfaces to improve users' performance. We develop an automatic method for web interface adaptation: by introducing index pages that minimize overall user browsing costs. The index pages are aimed at providing short cuts for users to ensure that users get to their objective web pages fast, and we solve a previously open problem of how to determine an optimal number of index pages. We empirically show that our approach performs better than many of the previous algorithms based on experiments on several realistic web log files.

Keywords: Adaptive web user interfaces; Clustering; Data mining; Web log mining 

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Copyright information

© Springer-Verlag London Limited 2002

Authors and Affiliations

  • Zhong Su
    • 1
  • Qiang Yang
    • 2
  • Hongjiang Zhang
    • 3
  • Xiaowei Xu
    • 4
  • Yu-Hen Hu
    • 5
  • Shaoping Ma
    • 1
  1. 1.State Key Lab of Intelligent Tech. and Systems, Tsinghua University, Beijing, ChinaCN
  2. 2.School of Computing Science, Simon Fraser University, Burnaby, BC, CanadaCA
  3. 3.Microsoft Research China, Beijing, ChinaCN
  4. 4.Siemens AG, Information and Communications Corporate Technology, Munich, GermanyDE
  5. 5.Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, Wisconsin, USAUS

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