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Recommendation of Web Pages using Weighted K-Means Clustering

by R. Thiyagarajan, K. Thangavel, R. Rathipriya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 14
Year of Publication: 2014
Authors: R. Thiyagarajan, K. Thangavel, R. Rathipriya
10.5120/15057-3517

R. Thiyagarajan, K. Thangavel, R. Rathipriya . Recommendation of Web Pages using Weighted K-Means Clustering. International Journal of Computer Applications. 86, 14 ( January 2014), 44-48. DOI=10.5120/15057-3517

@article{ 10.5120/15057-3517,
author = { R. Thiyagarajan, K. Thangavel, R. Rathipriya },
title = { Recommendation of Web Pages using Weighted K-Means Clustering },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 14 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number14/15057-3517/ },
doi = { 10.5120/15057-3517 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:15.467792+05:30
%A R. Thiyagarajan
%A K. Thangavel
%A R. Rathipriya
%T Recommendation of Web Pages using Weighted K-Means Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 14
%P 44-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web Recommendation Systems are implemented by using collaborative filtering approach. It is a specific type of information filtering system that aims to predict the user browsing activity and then recommend to the user web pages items that are likely to be of interest. In this paper, a new recommendation system is proposed by using Weighted K-Means clustering approach to predict the user's navigational behavior. The proposed recommendation system based on Weighted K-Means clustering performs well when compared to K-Means algorithm. The performance of the comparative analysis is presented through experimental results.

References
  1. Bamshad Mobasher,2001. WebPersonalizer: A Server-Side Recommender System Based on Web Usage Mining. Technical ReportTR01-010,Schoolof computer Science, telecommunications and Information Systems, DePaul University, Chicago, IL, USA
  2. C. P. Sumathi et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3046-3052
  3. AlMurtadha, Y. M. , M. N. B. Sulaiman, N. Mustapha and N. I. Udzir,2010. Mining web navigation profiles for recommendation system. Inform. Technol. J. , 9: 790-796. DOI:10. 3923/itj. 2010. 790. 796
  4. Castellano, G. , Fanelli, A. M. , & Torsello, M. A. (2011). NEWER: A system for NEuro-fuzzy Web recommendation. Applied soft Computing,11(1), 793-806.
  5. AlMurtadha, Y. ,Sulaiman, M. . N. B. , N. Mustapha and N. I. Udzir,(2011). IPACT: Improved web page recommendation System Using Profile Aggregation Based on Clustering of Transactions, American Journal of Applied Sciences, 8(3),277-283.
  6. Yahya AlMurtadha, Md. Nasir Sulaiman, Norwati Mustapha and Nur Izura Udzir. Improved web page recommender System Based on Web Usage Mining, Proceedings of the 3rd International Conference on Computing and Informatics, ICOCI 2011, 8-9 June 2011 Bandung, Indonesia, Paper No. 079.
  7. Ms. Vinita Shrivastava, Mr. Neetesh GuptaPerformance Improvement Of Web Usage Mining By Using Learning Based K-Mean Clustering on International Journal of Computer Science and its Applications-[ISSN 2250 - 3765].
  8. Khribi, M. K. , Jemni, M. , & Nasraoui, O. Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval (2009) , Educational Technology & Society, 12 (4), 30–42.
  9. F. Khalil, J. Li, H. Wang. An Integrated Model for Next Page Access Prediction, Copyright °c 2009 Inderscience Enterprises Ltd.
  10. Cooley, R. , B. Mobasher and J. Srivatsava, 1997. Web mining information and pattern discovery on the world wide web. Proceeding of the 9th IEEE International Conference on tools with Artificial Intelligence, Newport Beach,CA. , pp: 558-567. DOI: 10. 1109/TAI. 1997. 632303.
  11. Ms. Dipa Dixit, Mr. Jayant Gadge, Automatic Recommendation for Online Users Using Web Usage Mining on International Journal of Managing Information Technology (IJMIT) Vol. 2, No. 3, August 2010
  12. Dr. K. Thangadurai, M. Uma, Dr. M. Punithavalli, A Study On Rough Clustering Global Journal of Computer Science and Technology Vol. 10 Issue 5 Ver. 1. 0 July 2010 P a g e | 55
  13. Kyoung-jae Kim, Hyunchul Ahn, A Recommender system using GA K-means clustering in an online shopping market. . , Expert Systems with Applications (20070, doi:10,1016/j. eswa. 2006. 12. 025.
  14. Fuzhi ZHANG, Huilin LIU, jinbo CHAO, A Two-stage Recommendation Algorithm based on K-means Clustering in Mobile E-commerce, Journal of Computational Information Systems 6:10 (2010) 3327-3334.
Index Terms

Computer Science
Information Sciences

Keywords

Web Usage Mining Web recommendation system K-Means clustering Weighted K-Means clustering Hamming distance Mean square residue.

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