Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Clustering Frequent Navigation Patterns from Website Logs by Using Ontology and Temporal Information

  • Conference paper
  • First Online:
Computer and Information Sciences III

  • 1043 Accesses

Abstract

In this work, clustering algorithms are used in order to group similar frequent sequences of Web page visits. A new sequence is compared with all clusters and it is assigned to the most similar one. This work can be used for predicting and prefetching the next page user will visit or for helping the navigation of user in the website. They can also be used to improve the structure of website for easier navigation. In this study the effect of time spent on each web page during the session is also analyzed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.ceng.metu.edu.tr.

  2. 2.

    The original ontology is available at http://www.cs.umd.edu/projects/plus/SHOE/cs.html and he modified taxonomy used in this study is also available at http://www.ceng.metu.edu.tr/~sefa/msthesis/ontology.dat.

  3. 3.

    http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview.

  4. 4.

    http://www.ceng.metu.edu.tr.

References

  1. Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, pp. 33–40 (2001)

    Google Scholar 

  2. Facca, F.M., Lanzi, P.L.: Mining interesting knowledge from weblogs: a survey. Data Knowl. Eng. 53, 225–241 (2005). http://dl.acm.org/citation.cfm?id=1066378.1066379

  3. Heflin, J., Hendler, J., Luke, S.: SHOE a knowledge representation language for Internet applications. Technical Report CS-TR-4078 (UMIACS TR-99-71), Department of Computer Science, University of Maryland (1999)

    Google Scholar 

  4. Karypis, G.: CLUTO—a clustering toolkit. Technical Report #02-017 (Nov 2003)

    Google Scholar 

  5. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43, 142–151 (2000). doi:10.1145/345124.345169

    Google Scholar 

  6. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19, 17–30 (1989)

    Article  Google Scholar 

  7. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. Newsl. 1, 12–23 (2000). doi:10.1145/846183.846188

  8. Yilmaz, H., Senkul, P.: Using ontology and sequence information for extracting behavior patterns from web navigation logs. In: IEEE, ICDM Workshop on Semantic Aspects in Data Mining (SADM’10) (Dec 2010)

    Google Scholar 

  9. Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach. Learn. 55(3), 311–331 (2004)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by grant number TUBITAK-109E282, TUBITAK.

Author information

Authors and Affiliations

  1. METU Computer Engineering Department, 06800 , Ankara, Turkey

    Sefa Kilic, Pinar Senkul & Ismail Hakki Toroslu

Authors
  1. Sefa Kilic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pinar Senkul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ismail Hakki Toroslu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pinar Senkul .

Editor information

Editors and Affiliations

  1. , Dept of Electrical and Electronics Eng'g, Imperial College, London, SW7 2BT, United Kingdom

    Erol Gelenbe

  2. , EEE Dept., Imperial College, Exhibition Rd., London, SW72AZ, United Kingdom

    Ricardo Lent

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this paper

Cite this paper

Kilic, S., Senkul, P., Toroslu, I.H. (2013). Clustering Frequent Navigation Patterns from Website Logs by Using Ontology and Temporal Information. In: Gelenbe, E., Lent, R. (eds) Computer and Information Sciences III. Springer, London. https://doi.org/10.1007/978-1-4471-4594-3_37

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4594-3_37

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4593-6

  • Online ISBN: 978-1-4471-4594-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation