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Web Navigation Patterns Mining Based on Clustering of Paths and Pages Content

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Advanced Web and Network Technologies, and Applications (APWeb 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3842))

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Abstract

Combining the paths similarity and the pages content similarity, a novel clustering algorithm is presented. The actions character of users is revealed more exactly by clustering. The data scale is reduced by a long way during clustering. Based on the clusters, the user navigation patterns are generated by mining the Web log. The experiment result shows that the user navigation interest conversion patterns mined from Web log are typical and intuitionistic.

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References

  1. Wang, S., Gao, W., et al.: Path clustering: Discovering the Knowledge in the Web Site. Journal of Computer Research & Development 38, 482–486 (2001)

    Google Scholar 

  2. Mobasher, B., Cooley, R., Srivastava, J.: Creating Adaptive Web Sites through Usage-Based Clustering of URLs. In: IEEE Knowledge and Data Engineering Workshop (KDEX 1999), pp. 32–37. IEEE Press, New York (1999)

    Google Scholar 

  3. Larsen, B., Aone, C.: Fast and Effective Text Mining Using Linear-Time Document Clustering. In: Proc. of the 5th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining (KDD 1999), San Diego, pp. 16–22 (1999)

    Google Scholar 

  4. Büchner, A.G., Mulvenna, M.D.: Discovering Behavioral Patterns in Internet Log Files: Playing the Devil’S Advocate. In: Proc. of the 12th Biennial Int’l Telecommunications Society Conf . (ITS 1998), Stockholm (1998)

    Google Scholar 

  5. Han, J., Pei, J., et al.: Prefix Span: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proc. 2001 Int’l Conf. on Data Engineering (ICDE 2001), Heidelberg, pp. 215–224 (2001)

    Google Scholar 

  6. Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. In: Knowledge and Information Systems, vol. 1, pp. 5–32. Springer, London (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Science & Technology, Harbin Engineering University, Harbin, 150001, China

    Gang Feng, Guang-Sheng Ma & Jing Hu

Authors
  1. Gang Feng
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  2. Guang-Sheng Ma
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  3. Jing Hu
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

    Heng Tao Shen

  2. School of Computer Science and Technology, Heilongjiang University, P.O. Box, 150080, Harbin, China

    Jinbao Li

  3. Department of Computer Science and Engineering, Shanghai Jiatong University, 80 Dongcuan Road, 200240, Shanghai, China

    Minglu Li

  4. Department of Computer Science, College of Liberal Arts and Science, University of Iowa, 52242, Iowa City, IA, USA

    Jun Ni

  5. UNC Chapel Hill,  

    Wei Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Feng, G., Ma, GS., Hu, J. (2006). Web Navigation Patterns Mining Based on Clustering of Paths and Pages Content. In: Shen, H.T., Li, J., Li, M., Ni, J., Wang, W. (eds) Advanced Web and Network Technologies, and Applications. APWeb 2006. Lecture Notes in Computer Science, vol 3842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11610496_118

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  • DOI: https://doi.org/10.1007/11610496_118

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31158-4

  • Online ISBN: 978-3-540-32435-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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