Clustering Web Log Data Using Graph Partitioning and Agglomerative Hierarchical Algorithms for Predicting User Navigation Patterns

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Web usage mining enables organizations and website owners to study the user access patterns or behaviors when navigating their websites. In predicting user navigation patterns, previous study has proposed a two-stage clustering-and-classification of web log data. The main issues in web usage mining is the precision of recommendations in user navigation patterns since it’s will affect the quality of prediction of user future navigation. The quality and precision of navigation patterns produce in the clustering stage is useful contribution in designing an accurate user prediction system. This paper aims to undertake comparative analysis on two clustering algorithms, which are graph partitioning and agglomerative hierarchical clustering to compare user navigation patterns produced by each clustering technique. The results from clustering experiments showed that graph partitioning algorithm produced a detailed list of navigation patterns compared to agglomerative hierarchical clustering.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Web Usage Mining; Clustering; Graph Partitioning; Agglomerative Hierarchical Clustering

Full Text:



U. Maheswari, V.A. Siromoney, and K.M. Mehata, The Variable Precision Rough Set Model for Web Usage Mining, (Lecture Notes in Computer Science, Springer Verlag, 2198, pp. 520-524, 2001).

J. Han and M. Kamber, Data Mining: Concepts and Techniques, (Morgan Kaufmann Publishers, 2001).

C. Robert, B. Mobasher, and J. Srivastava, Data Preparation for Mining World Wide Web Browsing Patterns, (Knowledge and Information System, 1999).

J. Bollen, H.v.d. Sompel, L.M. Rocha, Mining Associative Relations from Website Logs and Their Application to Context-Dependent Retrieval using Spreading Activation. (WOWS, pp 55-66, 1999).

I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, Model-based Clustering and Visualization of Navigation Patterns on a Web Site, Data Mining and Knowledge Discovery, (Volume 7, Issue 4, pp 399-424, October 2003).

R. Baraglia and F. Silvestri, An Online Recommender System for Large Web Sites, (Proceedings of the Web Intelligence, IEEE/WIC/ACM International Conference, pp. 199-205, 2004).

Khairudin, N.M., Muda, Z., Mustapha, A., Nagarathinam, Y., Salleh, M.S., Clustering network traffic utilization, (2013) International Review on Computers and Software (IRECOS), 8 (5), pp. 1076-1081.

M. Jalali, N. Mustapha, A. Mamat, and M.N. Sulaiman, A New Clustering Approach based on Graph Partitioning for Navigation Patterns Mining, (Pattern Recognition. 19th International Conference, Dec. 2008).

V. Sujatha, Punithavalli, Improved User Navigation Pattern Prediction Technique from Web Log Data, (International Conference on Communication Technology and System Design, Procedia Engineering 30 pp. 92 – 99, 2012).

B. Hendrickson and R. Leland, A Multilevel Algorithm for Partitioning Graphs. (In Proc. Supercomputing ’95, December 1995).

A.K. Jain, M.N. Murty, and P.J. Flynn, Data Clustering: A Review. (ACM Computing Surveys, 31(3), pp. 275-277, 1999).

T.W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal, From User Access Patterns to Dynamic Hypertext Linking. (Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems, pp.1007-1014, May 1996).

D. He, A. Göker, and D.J. Harper, Combining Evidence for Automatic Web Session Identification, (Information Processing & Management, Volume 38, Issue 5, pp. 727–742, Sept 2002).

M. Perkowitz and O. Etzioni, Towards Adaptive Web Sites: Conceptual Framework and Case Study, (Artificial Intelligence, vol. 118, pp. 245-275, 2000).

C.R. Anderson, P. Domingos, and D.S.Weld, Personalizing Web Sites for Mobile Users, (WWW-01, May 2001).

M. Jalali, N. Mustapha, A. Mamat, and M.N. Sulaiman, WebPUM: A Web-based recommendation system to predict user future movements, (Expert Systems with Applications Volume 37, Issue 9, pp. 6201–6212, September 2010).

J. Han and M. Kamber, Data Mining, Second Edition: Concepts and Techniques (Elsevier Inc 2006).

R.R. Sokal, and P.H.A Sneath, The Principles of Numerical Taxanomy. (Freman San Francisco, p359, 1963).

P. Lingras and X. Huang, Statistical, Evolutionary, and Neurocomputing Clustering Techniques: Cluster-Based Versus Object-Based Approaches. (Intelligence Review 2002).

S. Theodo and B. Trousse, Application of the 2-3 Agglomerative Hierarchical Classification on Web Usage Data, (AxIS Research Team, INRIA Sophia-Antipolis, France, 2004).

M. Dimitrijevi, Z. Bosnjak, Web Usage Association Rule Mining System, (Interdisciplinary Journal of Information, Knowledge, and Management Volume 6, 2011.)

C. Pohle and M. Spiliopoulou, Building and Exploiting Ad Hoc Concept Hierarchies for Web Log Analysis, (DaWaK 2002. LNCS, vol. 2454, Springer, Heidelberg, 2002)

K.A. Smith and A. Ng, Web Page Clustering using a Self-Organizing Map of User Navigation Patterns, (Decision Support Systems. Volume 35, Issue 2. pp. 245-256, May 2003).


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize