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WUM: A Tool for Web Utilization Analysis

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1590)

Abstract

The navigational behaviour of users in the web is essential for the providers of information, services and goods. Search engines can help a user find a provider of interest, but it is the proper organization of the provider’s site that stimulates the user’s propensity to consume. To verify whether the site is effectively organized, knowledge on the navigation patterns occuring during visits to the site must be obtained. Our Web Utilization Miner WUM can assist in obtaining this knowledge. WUM is a mining system for the discovery of navigation patterns. A navigation pattern is a directed graph that summarizes the traversal movements of a group of visitors and satisfies certain human-centric criteria that make it “interesting”. Instead of focussing the mining process on the statistically dominant but not always interesting patterns, WUM supports the specification of interestingness criteria on their structure, content and statistics.

WUM provides a declarative mining language, MINT, with which the human expert can specify interestingness criteria on the fly. To discover the navigation patterns satisfying the expert’s criteria, WUM exploits an innovative aggregated storage representation for the information in the web server log.

Keywords

  • Association Rule
  • Sequential Pattern
  • Dummy Node
  • Pattern Descriptor
  • Page Access

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supported by the German Research Society, Berlin-Brandenburg Graduate School in Distributed Information Systems (DFG grant no. GRK 316).

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References

  1. Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE, Taipei, Taiwan (March 1995)

    Google Scholar 

  2. Amir, A., Feldman, R., Kashi, R.: A new and versatile method for association generation. Information Systems 22, 333–347 (1997)

    MATH  CrossRef  Google Scholar 

  3. Chen, M.-S., Park, J.S., Yu, P.S.: Data mining for path traversal patterns in a web environment. In: ICDCS, pp. 385–392 (1996)

    Google Scholar 

  4. Cooley, R., Mobasher, B., Srivastava, J.: Grouping web page references into transactions for mining world wide web browsing patterns. Technical Report TR 97-021, Dept. of Computer Science, Univ. of Minnesota, Minneapolis, USA (June 1997)

    Google Scholar 

  5. Cooley, R., Mobasher, B., Srivastava, J.: Web mining: Information and pattern discovery on the world wide web. In: ICTAI 1997 (December 1997)

    Google Scholar 

  6. Feldman, R., Klösgen, W., Zilberstein, A.: Visualization techniques to explore data mining results for document collections. In: KDD 1997, pp. 16–23. AAAI Press, Menlo Park (1997)

    Google Scholar 

  7. Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurences. In: KDD 1996, pp. 146–151 (1996)

    Google Scholar 

  8. Pirolli, P., Pitkow, J., Rao, R.: Silk from a sow’s ear: Extracting usable structures from the web. In: CHI 1996, Vancouver, Canada (April 1996)

    Google Scholar 

  9. Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Eng. 8(6), 970–974 (1996)

    CrossRef  Google Scholar 

  10. Spiliopoulou, M.: The laborious way from data mining to web mining (June 1998) (submitted)

    Google Scholar 

  11. Spiliopoulou, M., Lukas Faulstich, C., Winkler, K.: Discovering Interesting Navigation Patterns over Web Usage Data (1998) (submitted)

    Google Scholar 

  12. Tauscher, L., Greenberg, S.: Revisitation patterns in world wide web navigation. In: CHI 1997, Atlanta, Georgia (March 1997)

    Google Scholar 

  13. Wexelblat, A.: An environment for aiding information-browsing tasks. In: Proc. of AAAI Spring Symposium on Acquisition, Learning and Demonstration: Automating Tasks for Users. AAAI Press, Birmingham (1996)

    Google Scholar 

  14. Wexelblat, A.: Footprints: History-Rich Social Navigation. PhD thesis, MIT Media Laboratory (December 1997)

    Google Scholar 

  15. Winkler, K.: Realization and testing of a data mining environment for the discovery of user navigation patterns in a web site. Master’s thesis, Institut für Wirtschaftsinformatik, HU Berlin (October 1998) (to appear on german)

    Google Scholar 

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© 1999 Springer-Verlag Berlin Heidelberg

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Spiliopoulou, M., Faulstich, L.C. (1999). WUM: A Tool for Web Utilization Analysis. In: Atzeni, P., Mendelzon, A., Mecca, G. (eds) The World Wide Web and Databases. WebDB 1998. Lecture Notes in Computer Science, vol 1590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704656_12

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  • DOI: https://doi.org/10.1007/10704656_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65890-0

  • Online ISBN: 978-3-540-48909-2

  • eBook Packages: Springer Book Archive