Efficient Web Usage Mining Based on K-Medoids Clustering Technique
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.
[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
[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
@article{"International Journal of Information, Control and Computer Sciences:71700", author = "P. Sengottuvelan and T. Gopalakrishnan", title = "Efficient Web Usage Mining Based on K-Medoids Clustering Technique", abstract = "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, K-medoids, Recommendation, User
Session, Web Usage Mining.", volume = "9", number = "4", pages = "1044-5", }
