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Matrix based Fuzzy Clustering for Categorization of Web Users and Web Pages

by G. Sudhamathy, C. Jothi Venkateswaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 14
Year of Publication: 2012
Authors: G. Sudhamathy, C. Jothi Venkateswaran
10.5120/6175-8602

G. Sudhamathy, C. Jothi Venkateswaran . Matrix based Fuzzy Clustering for Categorization of Web Users and Web Pages. International Journal of Computer Applications. 43, 14 ( April 2012), 43-47. DOI=10.5120/6175-8602

@article{ 10.5120/6175-8602,
author = { G. Sudhamathy, C. Jothi Venkateswaran },
title = { Matrix based Fuzzy Clustering for Categorization of Web Users and Web Pages },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 14 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 43-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number14/6175-8602/ },
doi = { 10.5120/6175-8602 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:27.015947+05:30
%A G. Sudhamathy
%A C. Jothi Venkateswaran
%T Matrix based Fuzzy Clustering for Categorization of Web Users and Web Pages
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 14
%P 43-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Categorization of Web Users and Web Pages are the fundamental tasks of Web Personalization. In this paper it is proposed a Matrix Based Fuzzy Clustering Approach MBFCA and experimentally evaluated the approach for the effective discovery of web user clusters and web page clusters. The use of MBFCA enables the generation of clusters that can capture the Web user's navigation behavior based on their interest. In web usage analysis, many a times there are no sharp boundary between clusters. Hence fuzzy clustering is better suited for Web Usage Mining. Experimental results presented, the clusters generated by applying MBFCA, are intended to be used to make recommendations by suggesting interesting links to the user.

References
  1. Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, 2nd ed. , China Machine Press, Beijing, China, 2007.
  2. Han, Q. , Gao, X. , Wu, W. : Study on Web Mining Algorithm Based on Usage Mining. In: 9th International Conference on Computer-Aided Industrial Design and Conceptual Design, CAID/CD November 2008.
  3. Ying-Gui Li and Gui-Ji LI, Web Log Mining Based On Fuzzy Clustering, Computer Science, China, 2004, 31(12):97-100.
  4. Jing-Gong Li and Xiang-Gong WangS. Zhang, Fuzzy Set Theory And Application, Chain Science Press, Beijing, China, 2004.
  5. M. J. Martin-Bautista, D. H. Kraft, M. A. Vila, J. Chen and J. Cruz, "User profiles and fuzzy logic for Web retrieval issues," in Soft Computing Journal, 2004, vol. 6, number 5, pp. 365-372.
  6. D. Arotaritei and S. Mitra, "Web Mining: a survey in the fuzzy framework," in Fuzzy Sets and System, 2000, vol. 148, pp. 5-19.
  7. Facca, F. M. , & Lanzi, P. L. (2005). Mining Interesting Knowledge from Weblogs: A Survey, 53, 225–241.
  8. RENATA IVANCSY and FERENC KOVACS, "Clustering Techniques Utilized in Web Usage Mining," in Proceedings of the 5th WSEAS Int. Conf. on Artificial Inlelligence, Knowledge Engineering and Data Bases, Madrid, Spain, February 15-17, 2006, pp. 237-242.
  9. S. Araya, M. Silva and R. Weber, "A methodology for web usage mining and its application to target group identification," in Fuzzy Sets and Systems, 2004, vol. 148, pp. 139-152.
  10. Sudhamathy, G. 2010. Mining web logs: an automated approach. In Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in india (Coimbatore, India, September 16 - 17, 2010). A2CWiC '10. ACM, New York, NY, 1-4. DOI= http://doi. acm. org/10. 1145/1858378. 1858435
  11. Mobasher, B. , Cooley, R. , Srivastava, J. : Automatic Personalization Based on Web Usage Mining (2000), http://maya. cs. depaul. edu/~mobasher/personalization/
  12. J. Srivastava, R. Cooley, M. Deshpande and P. N. Tan, "Web usage mining: discovery and applications of usage patterns from web data," SIGKDD Explorations, 2000, vol. 1, number 2, pp. 12-23.
  13. A. Vakali, J. Pokorný and T. Dalamagas, "An Overview of Web Data Clustering Practices," EDBT Workshops, 2004, pp. 597-606.
  14. Xinlin Zhang, Xiangdong Yin "Design of an Information Intelligent System based on Web Data Mining", IEEE International Conference on Computer Science and Information Technology, 2008, p. 88-91.
  15. Chu-Hui Lee, Yu-Hsiang Fu "Web Usage Mining Based on Clustering of Browsing Features", IEEE Eighth International Conference on Intelligent Systems Design and Applications, 2008, p. 281-286.
  16. Thorleuchter, D. , Poel, D. V. D. , & Prinzie, A. (2012). Analyzing existing customers' websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing. Expert Systems with Applications, 39, 2597–2605.
Index Terms

Computer Science
Information Sciences

Keywords

Web Usage Mining Web Logs Clustering Web Personalization Fuzzy Logic Matrix Based Fuzzy Clustering

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