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Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs

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Web Intelligence: Research and Development (WI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2198))

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Abstract

Clustering plays a central role in segmenting markets. The identification of categories of visitors to a Web-site is very useful towards improved Web applications. However, the large volume involved in mining visitation paths, demands efficient clustering algorithms that are also resistant to noise and outliers. Also, dissimilarity between visitation paths involves sophisticated evaluation and results in large dimension of attribute-vectors. We present a randomized, iterative algorithm (a la Expectation Maximization or k-means) but based on discrete medoids. We prove that our algorithm converges and that has subquadratic complexity. We compare to the implementation of the fastest version of matrixbased clustering for visitor paths and show that our algorithm outperforms dramatically matrix-based methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Software Engineering, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Vladimir Estivill-Castro & Jianhua Yang

Authors
  1. Vladimir Estivill-Castro
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  2. Jianhua Yang
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Editor information

Editors and Affiliations

  1. Department of Systems and Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong

  2. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    Yiju Yao

  3. Department of Computer Science, Hong Kong Baptist University, 224 Waterloo Road, Kowloon, Hong Kong, China

    Jiming Liu

  4. Department of Information and Computer Science, Waseda University, 3-4-1 Okubo Shinjuku-Ku, Tokyo, 169, Japan

    Setsuo Ohsuga

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© 2001 Springer-Verlag Berlin Heidelberg

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Estivill-Castro, V., Yang, J. (2001). Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs. In: Zhong, N., Yao, Y., Liu, J., Ohsuga, S. (eds) Web Intelligence: Research and Development. WI 2001. Lecture Notes in Computer Science(), vol 2198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45490-X_64

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  • DOI: https://doi.org/10.1007/3-540-45490-X_64

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42730-8

  • Online ISBN: 978-3-540-45490-8

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