Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
Efficient Web Usage Mining Based on K-Medoids Clustering Technique

Authors: P. Sengottuvelan, T. Gopalakrishnan


Web Usage Mining is the application of data mining techniques to find usage patterns from web log data, so as to grasp required patterns and serve the requirements of Web-based applications. User’s expertise on the internet may be improved by minimizing user’s web access latency. This may be done by predicting the future search page earlier and the same may be prefetched and cached. Therefore, to enhance the standard of web services, it is needed topic to research the user web navigation behavior. Analysis of user’s web navigation behavior is achieved through modeling web navigation history. We propose this technique which cluster’s the user sessions, based on the K-medoids technique.

Keywords: Clustering, Web Usage Mining, recommendation, k-medoids, User Session

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1084


[1] Mojtaba Salehi, Isa Nakhai Kamalabadi, and Mohammed B. Ghaznavi Ghoushchi, “An effective Recommendation Framework for Personal Learning Environments using a Learner Preference Tree and a GA,” IEEE Transactions on learning technologies, vol. 6, No. 4,2013
[2] M. Salehi, M. Pourzaferani, and S.A. Razavi, “Hybrid Attribute-Based Recommender System for Learning Material Using Genetic Algorithm and a Multidimensional Information Model,” Egyptian Informatics J., vol. 14, no. 1, pp. 67-78, 2013.
[3] Yi Li,Jian Wang,Lin Mei, “A Personalized Recommendation System in E-Learning Environment based on Semantic Analysis”, Information Science and Service Science and Data Mining (ISSDM), 6th International Conference on New Trends 2012.
[4] J. kay, “Lifelong Learner Modeling for Lifelong Personalized Pervasive Learning,” IEEE Trans. Learning Technology, vol. 1, no. 4, pp. 215-228, Oct. 2008.
[5] Kumar, J. Nesbit, and K. Han, “Rating Learning Object Quality with Distributed Bayesian Belief Networks: The Why and the How,” Proc. Fifth IEEE Int’l Conf. Advanced Learning Technologies (ICALT ’05), pp. 685-687, 2005.
[6] N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, and R. Koper, “Recommender Systems in Technology Enhanced Learning,” Recommender Systems Handbook, P.B. Kantor, F. Ricci, L. Rokach, and B. Shapira, eds., pp. 387-415, Springer, 2011.
[7] Lai, H. & Yang, T. C. (2000), “A group-based inference approach to customized marketing on the web integrating clustering and association rules techniques” Hawaii International Conferenceon system sciences pp. 37 – 46.
[8] W. Chen, Z. Niu, X. Zhao, and Y. Li, “A Hybrid Recommendation Algorithm Adapted in E-Learning Environments,” World Wide Web, Sept. 2012, doi:10.1007/s11280-012-0187-z.
[9] F. Masseglia, P. Poncelet, and M. Teisseire, “Using data mining techniques on web access logs to dynamically improve hypertext structure”. In ACM SigWeb Letters, 8(3): 13-19, 1999.
[10] V elasquez, Bassi J D, YasudaA. “Mining Web data to create online navigation recommendations”. Data Mining, 2004:166-172. Proceedings of the Fourth IEEE International Conference on data mining (ICDM’04) 0-7695-2142-8/04 IEEE.
[11] R. Vaarandi, “A Data Clustering Algorithm for Mining Patterns from Event logs” in Proceedings of the 3rd IEEE Workshop on IP Operations and Management.
[12] Liu, F., Lu, Z. & Lu, S. (2001), `Mining association rules using clustering', Intelligent Data Analysis (5), 309 - 326.
[13] Cadez, I., Heckerman, D., Meek, C., Smyth, P. & White, S. (2003), “Model-based clustering and visualization of navigation patterns on a web site”, Data Mining and Knowledge Discovery .
[14] Lu, L., Dunham, M. & Meng, Y. (2005), “Discovery of significant usage patterns from clusters of clickstream Data”, WebKDD '05 .
[15] Zhu, J., Hong, J. & Hughes, J. G. (2002), “Using markov models for web site link prediction”, HT'02, USA pp. 69 - 170.
[16] Kim, D., Adam, N., Alturi, V., Bieber, M. & Yesha, Y. (2004), “A clickstream-based collaborative Filtering personalization model: Towards a better performance”, WIDM '04 pp. 88 - 95.
[17] Faten Khalil, Jiuyong Li, Hua Wang, "Integrating Recommendation Models for Improved Web Page Prediction Accuracy", in Proceedings of the thirty-first Australasian conference on Computer science, Vol. 74, 2008.
[18] P Sengouttuvelan, T Gopalakrishnan, V. S. Gowthami”A Pattern Recognition Technique for Learning Style Prediction System”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 9 (2015)
[19] S. Rafaeli, Y. Dan-Gur, and M. Barak, “Social Recommender Systems: Recommendations in Support of E-Learning,” Int’l J. Distance Education Technologies, vol. 3, no. 2, pp. 29-45, 2005.
[20] T Gopalakrishnan,Dr P Sengouttuvelan ,”Discovering user profiles for web personalization using EM with Bayesian Classification”Australian Journal of Basic and Applied Sciences, l.8(3) March 2014, Pages: 53-60