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Mining Web Sequential Patterns Using Reinforcement Learning

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Advanced Web Technologies and Applications (APWeb 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3007))

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Abstract

In this paper, the problem of discovering sequential patterns from the Web log is discussed and an algorithm of sequential patterns mining is brought forward. First, the data in the Web server log file is cleaned and the temporal set about every user is constructed. After the analysis of the temporal set, Markov decision process is applied to model Web log. Then, the sequential patterns of user behaviors are mined by means of reinforcement learning technology. Finally the experiment shows that our mining algorithm is effective.

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References

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Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Li Ning, Gao Yang, Tang Guifeng & Chen Shifu

Authors
  1. Li Ning
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  2. Gao Yang
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  3. Tang Guifeng
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  4. Chen Shifu
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Editor information

Editors and Affiliations

  1. Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

  2. The University of News South Wales, NSW 2052, Australia

    Xuemin Lin

  3. Department of Computer Science, Tsinghua University, 100084, Beijing, P.R. China

    Hongjun Lu

  4. Victoria University, Australia

    Yanchun Zhang

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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ning, L., Yang, G., Guifeng, T., Shifu, C. (2004). Mining Web Sequential Patterns Using Reinforcement Learning. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_106

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  • DOI: https://doi.org/10.1007/978-3-540-24655-8_106

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21371-0

  • Online ISBN: 978-3-540-24655-8

  • eBook Packages: Springer Book Archive

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