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Aggregate Profiling for Recommendation of web pages using SOM and K-Means Clustering Techniques

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 9
Year of Publication: 2011
Authors:
Shveta Kundra Bhatia
Harita Mehta
Veer Sain Dixit
10.5120/4518-6406

Shveta Kundra Bhatia, Harita Mehta and Veer Sain Dixit. Article: Aggregate Profiling for Recommendation of web pages using SOM and K-Means Clustering Techniques. International Journal of Computer Applications 36(9):13-20, December 2011. Full text available. BibTeX

@article{key:article,
	author = {Shveta Kundra Bhatia and Harita Mehta and Veer Sain Dixit},
	title = {Article: Aggregate Profiling for Recommendation of web pages using SOM and K-Means Clustering Techniques},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {36},
	number = {9},
	pages = {13-20},
	month = {December},
	note = {Full text available}
}

Abstract

Since, number of users are increasing exponentially so proper analysis of such data by devising efficient algorithms is essential which ultimately helps in determining the life time value of customers and judging the effectiveness of promotional campaigns as well. Better services and quality can be provided by mining the web access log files. In this paper, we have shown that with the help of clustering techniques, Self Organized Feature Maps and K-Means useful knowledge is extracted. We have also proposed to derive the interest and behavior of a significant group of users by applying the concept of “Aggregate Usage Profile”. Further, this technique has been used for looking frequently accessed pages for recommendations.

References

  • T. Kohonen, S. Kaski, K. Lagus, J. Salojarvi, J. Honkela, V. Paatero and A. Saarela. Self-organization of a massive document collection. IEEE Transactions on Neural Networks, 11(3):574–585, 2000.
  • Kate A. Smith and Alan Ng. Web page clustering using a Self-organizing map of user navigation patterns. Decision Support Systems, 35(2):245–256, 2003.
  • Web Usage Mining Using Self Organized Maps, Paola Britos, Damián Martinelli, Hernan Merlino, Ramón García-Martínez IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.6, June 2007.
  • X. Wanga, A. Abraham, K. A. Smitha. Intelligent web traffic mining and analysis. Journal of Network and Computer Applications, vol. 28, 2004, pp. 147–165.
  • R. Iváncsy, I. Vajk, Different Aspects of Web Log Mining. 6th International Symposium of Hungarian Researchers on Computational Intelligence. Budapest, Nov., 2005.
  • R. Kosala, H. Blockeel, Web Mining Research: A Survey, ACM SIGKKD Explorations, vol. 2(1), July 2000.
  • J. Srivastava, R. Cooley, M. Deshpande, P.-N. Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, SIGKKD Explorations, vol.1, Jan 2000.
  • B.Moshaber, R. Cooley, J. Srivastava, Automatic Personalization Based on Web Usage Mining, Communications of the ACM, vol.43 (8), 2000.
  • S. K. Pal, V Talwar, P Mitra. Web Mining in Soft Computing Framework: Relevance. State of the Art and Future Directions. IEEE Trans. on Neural Networks, vol.13 (5), 2002, pp. 1163–77.
  • Dehu Qi, Chung-Chih Li, Self-Organizing Map based Web Pages Clustering using Web Logs. Conference of Software Engineering and Data Engineering 2007, 265-270.
  • Ranieri Baraglia and Fabrizio Silvestri, “An Online Recommender System for Large Web Sites”, Web Intelligence, 2004 Proceedings. IEEE/WIC/ACM International Conference on 20-24 Sept. 2004
  • Web Data Mining Research: A Survey, Brijendra Singh, Hemant Kumar Singh.IEEE 2010 Conference.
  • S. Santhi, Dr. Purushothaman Srinivasan, “An Improved Usage Mining using Back Propagation Algorithm with Functional Update”, 2009 IEEE International Conference Advance Computing Conference (IACC 2009), 978-1-4244- 2928-8/09.
  • Hanan Ettaher Dagez &Mhd Sapiyan Baba, “Applying Neural Network Technology in Qualitative Research for Extracting Learning Style to Improve E-Learning Environment, The IEEE International Conference, 978-1- 4244-2328-6/08 2008.
  • Hafidh Ba-Omar, Ilias Petrounias and Fahad Anwar, “A Framework ofWeb Usage Mining to Personalize E-Learning”, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) 0-7695- 2916-X/07 007 IEEE.
  • Zurina Muda and Ros Emiliana Kartina Mohamed “Adaptive User Interface Design In Multimedia Courseware” IEEE 0-7803-9521-2/06 2006.
  • Ekaterina Vasilyeva, Mykola Pechenizkiy, Seppo Puurone “Towards the Framework of Adaptive User Interfaces for eHealth”, Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05) 1063-7125/05 2005.
  • Pattern Discovery of Web Usage Mining. Nina, S.P. ; Rahman, M. ; Bhuiyan, K.I. ; Ahmed,K.; Comput. Sci. & Eng., Shahjalal Univ. of Sci. & Technol., Sylhet, Bangladesh. In: Computer Technology and Development, 2009. ICCTD '09. International Conference on 13-15 Nov. 2009 Volume: 1, 499.
  • Tsuyoshi Murata and Kota Saito “Extracting Users Interests from Web Log Data”, Proceedings of the 2006 IEEE/WIC/ACM International Conference of Web Intelligence (WI 2006 Main Conference Proceedings) (WI’06) 2006 IEEE.
  • 20] Web usage mining: Discovery of the users' navigational patterns using SOM.Etminani, K. ; Delui, A.R. ; Yanehsari, N.R. ; Rouhani, M. ; Dept. of Comp. Eng., Ferdowsi Univ. of Mashhad, Mashhad, Iran. In:Networked Digital Technologies, 2009. NDT '09. First International Conference on 28-31 July 2009. Page 224.
  • Universal log file analysis and reporting, available at: http://www.sawmill.net
  • Page Cluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation: Jianhan Zhu, Jun Hong, John G. Hughes; published in Journal ACM Transactions on Internet Technology.Volume 4 Issue 2, May 2004.
  • A Dynamic Clustering-Based Markov Model for Web Usage Mining; Jos_e Borges, Mark Levene Birkbeck, May 26, 2004.
  • Jalali, M., Mustapha, N., Mamat, A., and Sulaiman, M.N. A new clustering approach based on graph partitioning for navigation patterns mining. In Proceedings of ICPR. 2008, 1-4.
  • Mehdi Hosseini, Hassan Abol Hassani, “Mining Search Engine Query Log for evaluating Content and Structure of a Web Site” in Proceedings of the 2007 IEEE/WIC/ACM International Conference on Web Intelligence.
  • Chu-Hui Lee, Yu-Hsiang Fu, "Web Usage Mining Based on Clustering of Browsing Features," isda, vol. 1, pp.281-2862008 Eighth International Conference on Intelligent Systems Design and Applications, 2008.
  • KobraEtminani,Mohammad-R. Akbarzadeh-T., Noorali Raeeji Yanehsari, “Web Usage Mining: users' navigational patterns extraction from web logs using Ant-based Clustering Method”, IFSA-EUSFLAT 2009.
  • DeMin Dong, "Exploration on Web Usage Mining and its Application", International Workshop on Intelligent Systems and Applications, Pp. 1-4, 2009.
  • N. Sujatha, K. Iyakutty, “Refinement of Web usage Data Clustering from K-means with Genetic Algorithm”, European Journal of Scientific Research ISSN 1450-216X Vol.42 No.3 (2010), pp.464-476.
  • Written by Cao Thang in Soft Intelligent Laboratory, Ritsumeikan University, 2003-200.
  • HaritaMehta,Shveta Kundra Bhatia, Punam Bedi,V.S.Dixit, “Collaborative Personalized WebRecommender Systemusing Entropy based Similarity Measure”, International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011.