CFP last date
20 January 2025
Reseach Article

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

by Ramin Afshoon, Ali Harounabadi, Javad Mir Abedini
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 4
Year of Publication: 2014
Authors: Ramin Afshoon, Ali Harounabadi, Javad Mir Abedini
10.5120/17175-7264

Ramin Afshoon, Ali Harounabadi, Javad Mir Abedini . Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering. International Journal of Computer Applications. 98, 4 ( July 2014), 42-47. DOI=10.5120/17175-7264

@article{ 10.5120/17175-7264,
author = { Ramin Afshoon, Ali Harounabadi, Javad Mir Abedini },
title = { Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 4 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number4/17175-7264/ },
doi = { 10.5120/17175-7264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:22.169181+05:30
%A Ramin Afshoon
%A Ali Harounabadi
%A Javad Mir Abedini
%T Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 4
%P 42-47
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Researchers have studied qualitative and quantitative methods to assess the quality of website. Previous studies had determined criteria such as quality of service. Human behavior, namely the objective perspective, is the essential source to obtain human thinking and real doings. For this reason, data mining approaches are used to acquire the objective source. In this research, proposed subtractive clustering is applied in evaluating educational web sites from the fuzzy objective perspective. An empirical study is carried out to validate the model capability. Results indicate that in the recommended algorithm are closer to the real data.

References
  1. Lin, H. F, "Measuring online learning systems success: Applying the updated DeLone and McLean's model", Cyber Psychology and Behavior, Vol. 10, Issue. 6, pp. 817–820, 2007.
  2. Mustapasa, O. , Karahoca, D. , Karahoca, A. , Yucel, A. , Uzunboylu, H. 2010. Implementation of semantic web mining on e-learning. In: proc. of Social and Behavioral Sciences, vol. 2, Issue 2, pp. 5820-5823.
  3. Jain, A. K. , Murty, M. N. , Flynn, P. J. 1999. Data clustering: a review. ACM Computing Surveys (CSUR), Vol. 31, Issue 3, pp. 264-323.
  4. Chiu, S. L. 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, Vol. 2, pp. 267-278.
  5. Bataineh, K. M, Naji, M, Seqar, M. 2011. A comparison study between various fuzzy clustering algorithms. Jordan Journal of Mechanical and Industrial Engineering, Vol. 5, No. 4, pp. 335-343.
  6. Guo, L. , Zhang, M. , Sun, L. , Wang, Z. 2006. Fuzzy clustering model of CRM in securities trade. Proceedings of the 6thWorld Congress on Intelligent Control and Automation (WCICA), pp. 6052-6054.
  7. Huang, T. C, Huang, C. 2010. An integrated decision model for evaluating educational web sites from the fuzzy subjective and objective perspectives. Computers & Education. Elsevier, Vol. 55, pp. 616-629.
  8. Pamutha, T. , Chimphlee, S. , Kimpan, C. , Sanguansat, P. 2012. "Data preprocessing on web server log files for mining users access patterns". International Journal of Research and Reviews in Wireless Communications (IJRRWC) Vol. 2, No. 2, pp. 92-98.
  9. Santra, A. K. , Jayasudha, S. 2012. Classification of web log data to identify interested users using naïve Bayesian classification. International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, pp. 381-387.
  10. Demirli, K. , Cheng, S. X. , Muthukumaran, P. 2003. Subtractive clustering based modeling of job sequencing with parametric search. Fuzzy Sets and Systems, Vol. 137, Issue 2, pp. 235–270.
  11. Cooley, R. , Mobasher, R. , Srivastava, J. 1999. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, vol. 1, pp. 5–32.
  12. Heeact. 2009. Higher education evaluation & accreditation council of Taiwan. [WWW document]. Available from. http://www. heeact. edu. tw/mp. asp?mp¼4
  13. Sahebi S. , Oroumchian F. and Khosravi R. , 2008, an enhanced similarity measure for utilizing site structure in web personalization systems, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, IEEExplore, pp. 82-85.
  14. MATLAB Software. http://www. mathworks. com.
  15. Huang, T. C, Huang, C. 2010. An integrated decision model for evaluating educational web sites from the fuzzy subjective and objective perspectives. Computers & Education. Elsevier, Vol. 55, pp. 616-629.
Index Terms

Computer Science
Information Sciences

Keywords

Web site quality Data mining Subtractive clustering