Skip to main content
SpringerLink
Log in

Mining Navigation Patterns Using a Sequence Alignment Method

  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract.

In this article, a new method is illustrated for mining navigation patterns on a web site. Instead of clustering patterns by means of a Euclidean distance measure, in this approach users are partitioned into clusters using a non-Euclidean distance measure called the Sequence Alignment Method (SAM). This method partitions navigation patterns according to the order in which web pages are requested and handles the problem of clustering sequences of different lengths. The performance of the algorithm is compared with the results of a method based on Euclidean distance measures. SAM is validated by means of user-traffic data of two different web sites. Empirical results show that SAM identifies sequences with similar behavioral patterns not only with regard to content, but also considering the order of pages visited in a sequence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. 19991 Borges J, Levene M (1999) Data mining of user navigation patterns. In Web usage analysis and user profiling: 92-111. Lecture notes in computer science, vol 1836. Springer, Berlin

    Google Scholar 

  2. 19992 Büchner AG, Baumgarten M, Anand SS et al (1999) Navigation pattern discovery from internet data. In ACM workshop on web usage analysis and user profiling (Webkdd), San Diego, CA, August 1999

  3. 19983 Büchner AG, Mulvenna MD (1998) Discovering internet marketing intelligence through online analytical web usage mining. SIGMOD Record 27(4): 54–61

    Google Scholar 

  4. 20004 Cadez I, Heckerman D, Meek C et al (2000) Visualization of navigation patterns on a web site using model based clustering. Technical Report, University of California

  5. 20015 Capri (2001) Generic sequence discovery product, http://www.mineit.com/products

  6. 19956 Catledge L, Pitkow J (1995) Characterizing browsing behaviors on the World Wide Web. Computer Networks and ISDN Systems 27(6): 1065–1073

    Article  Google Scholar 

  7. 20007 Cooley R (2000) Web usage mining: discovery and application of interesting patterns from web data. University of Minnesota, http://www-users.cs.umn.edu/cooley/pubs.html

  8. 19998 Cooley R, Mobasher B, Srivastava J (1999) Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems 1(1):5–32

    Google Scholar 

  9. 20009 Cooley R, Tan P-N, Srivastava J (2000) Discovery of interesting usage patterns from web data: advances in web usage analysis and user profiling. Lecture notes in computer science, vol 1836. Springer, Berlin

    Google Scholar 

  10. 198010 Everitt B (1980) Cluster analysis. Halsted Press, New York

  11. 199811 Hair JF, Andersen RE, Tatham RL et al (1998) Multivariate data analysis. Prentice-Hall, Engllwood Cliffs, NJ

  12. 2001a12 Joh CH, Arentze TA, Timmermans HJP (2001a) A position-sensitive sequence-alignment method illustrated for space-time activity-diary data. Environment and Planning A 33(2): 313–338

    Article  Google Scholar 

  13. 2001b13 Joh CH, Arentze TA, Timmermans HJP (2001b) Multidimensional sequence alignment methods for activity-travel pattern analysis: a comparison of dynamic programming and genetic algorithms. Geographical Analysis 33(3):247–270

    Google Scholar 

  14. 199814 Krishnapuram R, Joshi A (1998) Robust clustering methods to support web mining. In ACM SIGMOD workshop on research issues in data mining and knowledge discovery, Philadelphia, PA, June 1998

  15. 199715 Mannila H, Ronkainen P (1997) Similarity of event sequences. In Proceedings of the 4th international workshop on temporal representation and reasoning, TIME IEEE Computer Society, May 1997

  16. 199616 Mobasher B, Jain N, Han E, Srivastava J (1996) Web mining: pattern discovery from World Wide Web transactions. Technical Report TR 96-050, University of Minnesota, Department of Computer Science, Minneapolis

    Google Scholar 

  17. 200017 Mobasher B, Dai H, Luo T et al (2000) Discovery of aggregate usage profiles for web personalization. In Proceedings of the WebKDD Workshop, August 2000

  18. 200018 Mulvenna MD, Anand SS, Büchner AG (2000) Personalization on the net using web mining: introduction. Communications of ACM 43(8): 122–125

    Article  Google Scholar 

  19. 199919 Nasraoui O, Krishnapuram R, Anupam J (1999) Mining web access logs using a fuzzy relational clustering algorithm based on a robust estimator. In Proceedings of the 8th international fuzzy systems association congress, June 1999

  20. 199720 Perkowitz M, Etzioni O (1997) Adaptive web sites: an ai challenge. In International joint conference on AI, Nagoya, Japan, August 1997

    Google Scholar 

  21. 199821 Perkowitz M, Etzioni O (1998) Adaptive web sites: automatically synthesizing web pages. AAAI, July: 727–732

  22. 199622 Pirolli P, Pitkow J, Rao R (1996) Silk from a sow’s ear: extracting usable structures from the web. In conference on human factors in computing systems (CHI-96), April 1996

  23. 198323 Sankoff D, Kruskal JB (1983). Time warps, string edits and macromolecules: the theory and practice of sequence comparison. Addison-Wesley, Reading, MA

  24. 200024 Shahabi C, Faisal A, Kashani FB et al (2000) INSITE: a tool for real-time knowledge discovery from users web navigation. In Proceedings of 26th international conference on very large databases (VLDB), Cairo, Egypt, September 2000

  25. 199725 Shahabi C, Zarkesh A, Adibi J et al (1997) Knowledge discovery from users web-page navigation. In Proceedings of the 7th IEEE international conference on research issues in data engineering, June 1997

  26. 199826 Spiliopoulou M, Faulstich LC (1998) WUM: a tool for web utilization analysis. In EDBT workshop WebDB, March 1998. Springer (extended version in LNCS, vol 1590)

  27. 199927 Spiliopoulou M, Faulstich LC, Winkler K (1999) A data miner analysing the navigational behaviour of web users. In Proceedings of the workshop on machine learning in user modelling of the ACAI international conference, July 1999

  28. 200028 Spiliopoulou M, Pohle C, Faulstich L (2000) Improving the effectiveness of a web site with web usage mining. In Masand and Spiliopoulou: pp 139–159

    Google Scholar 

  29. 199629 Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In Proceedings of the 5th international conference on extending database technology, March 1996

    Google Scholar 

  30. 200030 Srivastava J, Cooley R, Deshpande M et al (2000) Web usage mining: discovery and applications of usage patterns from web data. In SIGKDD explorations 1(2): 12–23

    Google Scholar 

  31. 199831 Wilson WC (1998) Activity pattern analysis by means of sequence alignment methods. Environment and Planning A(30): 1017–103

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Data Analysis and Modelling Research Group, Limburg university, Universitaire Campus--gefouw D, B-3590, Diepenbeek, Belgium

    Birgit Hay, Geert Wets & Koen Vanhoof

Authors
  1. Birgit Hay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geert Wets
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koen Vanhoof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Birgit Hay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hay, B., Wets, G. & Vanhoof, K. Mining Navigation Patterns Using a Sequence Alignment Method. Knowledge and Information Systems 6, 150–163 (2004). https://doi.org/10.1007/s10115-003-0109-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-003-0109-6

Keywords

Search

Navigation