Skip to main content
SpringerLink
Log in

Mining and Analysis of Clickstream Patterns

  • Chapter
Foundations of Computational, IntelligenceVolume 6

Part of the book series: Studies in Computational Intelligence ((SCI,volume 206))

Abstract

The explosive growth of the web has drastically changed the way in which information is managed and accessed. The large-scale of web data sources and the wide availability of services over the internet have increased the need for effective web data mining techniques and mechanisms . A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this work, we focus on web usage mining, applying data mining techniques to web server logs. Web usage mining is the non-trivial process of distinguishing implicit, previously unknown but potentially useful clickstream patterns that may exist in any collection of web access logs. The required abstraction can be generated by clustering the web access logs based on some sort of similarity measure. Clustering is done such that the web access logs within the same group or cluster are more similar than data points from different clusters. In this chapter, we propose a partitional algorithm namely Multi Pass Combined Standard Deviation(CSD) Means algorithm which automatically generates the optimum number of clusters from the web clickstream patterns. The quality of clusters obtained using these algorithms are compared using K-Means algorithm, Rough K-Means algorithm and model based algorithms ANTCLUST and ACCANTCLUST. The experimental analysis of mined clickstream patterns shows the effectiveness of the proposed algorithm.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abraham, A.: Natural Computation for Business Intelligence from Web Usage Mining. In: Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2005) (2005)

    Google Scholar 

  2. Baumgarten, M., Bchner, A.G., Anand, S.S., Mulvenna, M.D., Hughes, J.G.: Navigation Pattern Discovery from Internet Data. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS, vol. 1836. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  3. Bezdek, J.C.: Numerical Taxonomy with Fuzzy Sets. J. Math. Biol. 1, 57–71 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  4. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)

    MATH  Google Scholar 

  5. Cooley, R.: Web Usage Mining: Discovery and Application of Interesting Patterns from web data, Ph.D. Thesis, University of Minnesota (2000)

    Google Scholar 

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

    Google Scholar 

  7. Cooley, R., Srivastava, J., Mobasher, B.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 1997), pp. 558–567 (1997b)

    Google Scholar 

  8. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Analysis and Machine Intelligence 1(4), 224–227 (1979)

    Article  Google Scholar 

  9. Pierrakos, D., Paliouras, G.O., Papatheodorou, C., Spyropoulos, C.D.: Web Usage Mining as a Tool for Personalization: A Survey. User Modeling and User-Adapted Interaction 13, 311–372 (2003)

    Article  Google Scholar 

  10. Dubes, R., Jain, A.K.: Validity studies in clustering methodologies. Pattern Recognition 11(1), 235–253 (1979)

    Article  MATH  Google Scholar 

  11. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley Interscience, New York (1973)

    MATH  Google Scholar 

  12. Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-Separated Clusters. Journal Cybern. 3(3), 32–57 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  13. Estivill-Castro, V., Yang, J.: Fast and robust general purpose clustering algorithms. In: Pacific Rim International Conference on Artificial intelligence, pp. 208–218 (1979)

    Google Scholar 

  14. Flake, G.W., Lawrence, S., Lee Giles, C., Coetzee, F.M.: Self- organization and identification of Web communities. IEEE Computer 35(3), 66–71 (2002)

    Google Scholar 

  15. Fu, Y., Sandhu, K., Shi, M.: Clustering of web users based on access patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 21–38. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  16. Pallis, G., Angelis, L., Vakali, A.: Validation and interpretation of Web users’ sessions clusters. In: Information Processing and Management (2006)

    Google Scholar 

  17. Peters, G.: Some refinements of rough k-means clustering. Pattern Recognition 39, 1481–1491 (2006)

    Article  MATH  Google Scholar 

  18. Hannah Inbarani, H., Thangavel, K.: Clickstream Intelligent Clustering using Accelerated Ant Colony Algorithm. In: Advanced Computing and Communications, 2006. ADCOM 2006. International Conference I, pp. 129–134 (2006)

    Google Scholar 

  19. Hannah Inbarani, H., Thangavel, K., Pethalakshmi, A.: Rough Set Based Feature Selection for Web Usage Mining. In: Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), pp. 33–38 (2007) ISBN:0-7695-3050-8

    Google Scholar 

  20. Heer, J., Chi, E.: Mining the structure of user activity using cluster stability. In: Proceedings of the Workshop on Web Analytics, SIAM Conference on Data Mining, Arlington, VA (April 2002)

    Google Scholar 

  21. Chang, H.-J., Hung, L.-P., Ho, C.-L.: An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Systems with Applications 32, 753–764 (2007)

    Article  Google Scholar 

  22. Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)

    Article  Google Scholar 

  23. Kuo, R.J., Wang, H.S., Hu, T.-L., Chou, S.H.: Application of Ant K-Means on Clustering Analysis. Computers and Mathematics with Applications 50, 1709–1724 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  24. Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough K-means. Journal of Intelligent Information Systems (2002)

    Google Scholar 

  25. McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Le Cam, L.M., Newman, J. (eds.) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  26. Mobasher, B., Cooley, R., Srivastava, J.: Creating adaptive web sites through usage-based clustering of URLs. In: Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX) (1999)

    Google Scholar 

  27. Labroche, N., Monmarche, N., Venturini, G.: A new clustering algorithm based on the chemical recognition system of ants. In: Proceedings of 15th European Conference on Artificial Intelligence (ECAI 2002), Lyon FRANCE, pp. 345–349 (2002)

    Google Scholar 

  28. Labroche, N., Monmarche, N., Venturini, G.: Web session clustering with artificial ants colonies’. In: Proc. of WWW 2003, May 20-24 (2003)

    Google Scholar 

  29. Perkowitz, M., Etzioni O.: Adaptive sites: automatically learning from user access patterns. In: Proceedings of WWW6 (1997), www.scope.gmd.de/info/www6/posters/722/index.html

  30. Song, Q., Shepperd, M.: Mining web browsing patterns for E-commerce. Computers in Industry 57, 622–630 (2006)

    Article  Google Scholar 

  31. Bucklin, R.E., Lattin, J.M., Ansari, A., Gupta, S., Bell, D., Coupey, E., Little, J.D.C., Mela, C., Montgomery, A., Steckel, J.: Choice And the Internet: From Clickstream to Research Stream. Marketing Letters 13(3), 245–258 (2002)

    Article  Google Scholar 

  32. Selamat, A., Sigeru, O.: Web page feature selection and classification using neural networks. Information Sciences 158, 69–88 (2004)

    Article  MathSciNet  Google Scholar 

  33. Song, A.-B., Zhao, M.-X., Liang, Z.-P., Dong, Y.-S., Luo, J.-Z.: Discovering user profiles for Web personalization recommendation. Journal of Computer Science and Technology 19(3), 320–328 (2004)

    Article  Google Scholar 

  34. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.T.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations 1(2), 2–23 (2000)

    Article  Google Scholar 

  35. Kumar De, S., Radha Krishna, P.: Clustering web transactions using rough approximation. Fuzzy Sets and Systems 148, 131–138 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  36. Mitra, S.: Rough-Fuzzy Collaborative Clustering. IEEE Transactions on Systems, Man and Cybernetics 36(4) (2006)

    Google Scholar 

  37. Thangavel, K., Ashok Kumar, D.: Pattern Clustering using Neural Network, Vision 2020: The Strategic role of Operational Research. Allied Publishers PVT LTD, New Delhi, pp. 662–679 (2006)

    Google Scholar 

  38. Titterington, D., Smith, A., Makov, U.: Statistical analysis of finite mixture distributions. John Wiley and Sons, Chichester (1985)

    MATH  Google Scholar 

  39. Voges, K.E., Pope, N.K.L., Brown, M.R.: Cluster analysis of marketing data examining online shopping orientation: a comparison of k-means and rough clustering approaches. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Heuristics and Optimization for Knowledge Discovery, pp. 207–224. Idea Group Publishing, Hershey (2002)

    Google Scholar 

  40. Wang, X., Abraham, A., Smith, K.: Intelligent web traffic mining and analysis. Journal of Network and Computer Applications 28(2), 147–165 (2005)

    Article  Google Scholar 

  41. WangBin, Liuzhijing: Web Mining Research. In: Proceedings of the Fifth nternational Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003) (2003)

    Google Scholar 

  42. Xing, W., Ghorbani, A.: Weighted PageRank Algorithm. In: Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR 2004) (2004)

    Google Scholar 

  43. W.W.W. Consortium. The Common Log file Format (1995), http://www.w3.org/Daemon/User/Config/Logging.html#common-logfile-format

  44. Xie, X.L., Beni, G.: A Validity Measure for fuzzy Clustering. IEEE Trans. on Pattern Analysis and MachineIntelligence 13(8), 841–847 (1991)

    Article  Google Scholar 

  45. Yan, T.W., Jacobsen, M., Garcia-Molina, H., Dayal, U.: From user access patterns to dynamic hypertext linking. In: Proceedings of 5th WWW, pp. 1007–1014 (1996)

    Google Scholar 

  46. Zhang, X., Gong, W., Kawamura, Y.: Customer behavior pattern discovering with web mining. In: Proceedings of Asia Pacific web conference, Hangzhou, China, pp. 844–853 (2004)

    Google Scholar 

  47. Pabarskaite, Z., Raudys, A.: A process of knowledge discovery from web log data: Systematization and critical review. Journal of Intelligent Information Systems 28, 79–104 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, 636 011,  

    H. Hannah Inbarani & K. Thangavel

Authors
  1. H. Hannah Inbarani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Thangavel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, P.O. Box 2259, 98071-2259, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

  4. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, 708 33, Ostrava, Czech Republic

    Václav Snášel

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Inbarani, H.H., Thangavel, K. (2009). Mining and Analysis of Clickstream Patterns. In: Abraham, A., Hassanien, AE., de Leon F. de Carvalho, A.P., Snášel, V. (eds) Foundations of Computational, IntelligenceVolume 6. Studies in Computational Intelligence, vol 206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01091-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01091-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01090-3

  • Online ISBN: 978-3-642-01091-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation