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International Conference on Advanced Data Mining and Applications

ADMA 2008: Advanced Data Mining and Applications pp 99–110Cite as

Mining Evolving Web Sessions and Clustering Dynamic Web Documents for Similarity-Aware Web Content Management

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5139)

Abstract

Similarity discovery has become one of the most important research streams in web usage mining community in the recent years. The knowledge obtained from the exercise can be used for many applications such as predicting user’s preference, optimizing web cache organization and improving the quality of web document pre-fetching. This paper presents an approach of mining evolving web sessions to cluster web users and establish similarities among web documents, which are then applied to a Similarity-aware Web content Management system, facilitating offline building of the similarity-ware web caches and online updating of sub-caches and cache content similarity profiles. An agent-based web document pre-fetching mechanism is also developed to support the similarity-aware caching to further reduce the bandwidth consumption and network traffic latency, therefore to improve the web access performance.

Keywords

  • Web usage mining
  • web caching
  • similarity discovery
  • web document pre-fetching

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Author information

Authors and Affiliations

  1. School of Computer and Information Science, Edith Cowan University, 2 Bradford Street, Mount Lawley, WA 6050, Australia

    Jitian Xiao

Authors
  1. Jitian Xiao
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Editor information

Editors and Affiliations

  1. School of Computer Science, Sichuan University, 610065, Chengdu, China

    Changjie Tang

  2. Department of Computer Science, The University of Western Ontario, Canada

    Charles X. Ling

  3. School of ITEE, The University of Queensland, Australia

    Xiaofang Zhou

  4. Faculty of Science & Engineering, York University, 355 Lumbers Building, M3J 1P3, Toronto, Ontario, Canada

    Nick J. Cercone

  5. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, 4072, Queensland, Australia

    Xue Li

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© 2008 Springer-Verlag Berlin Heidelberg

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Xiao, J. (2008). Mining Evolving Web Sessions and Clustering Dynamic Web Documents for Similarity-Aware Web Content Management. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_11

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  • DOI: https://doi.org/10.1007/978-3-540-88192-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88191-9

  • Online ISBN: 978-3-540-88192-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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