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A Classification Algorithm to Improve the Design of Websites

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DOI: 10.4236/jsea.2012.57057    5,965 Downloads   8,692 Views   Citations

ABSTRACT

In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Singh and B. Singh, "A Classification Algorithm to Improve the Design of Websites," Journal of Software Engineering and Applications, Vol. 5 No. 7, 2012, pp. 492-499. doi: 10.4236/jsea.2012.57057.

References

[1] H. K. Singh and B. Singh, “Web Data Mining Research: A Survey,” IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 28-29 December 2010, pp. 1-10. doi:10.1109/ICCIC.2010.5705758
[2] M. Jalali, N. Mustapha, et al., “WebPUM: A Web-Based Recommendation System to Predict User Future Movements,” International Journal Expert Systems with Applications, Vol. 37, No. 9, 2010, pp. 6201-6212. doi:10.1016/j.eswa.2010.02.105
[3] M. Eirinaki and M. Vazirgiannis, “Web Mining for Web Personalization,” ACM Transactions on Internet Technology, Vol. 3, No. 1, 2003, pp. 1-27. doi:10.1145/643477.643478
[4] S. K. Shinde and U. V. Kulkarni, “A New Approach for on Line Recommender System in Web Usage Mining,” IEEE International Conference on Advanced Computer Theory and Engineering, Phuket Island, 20-22 December 2008. doi:10.1109/ICACTE.2008.72
[5] R. Lakshmipathy, V. Mohanraj, et al., “Capturing Intuition of Online Users using a Web Usage Mining,” IEEE International Advance Computing Conference, Patiala, 6-7 March 2009. doi:10.1109/IADCC.2009.4809222
[6] W. T. Yan, M. Jacobsen and H. Garciamolina, “From User Access Patterns to Dynamic Hypertext Linking,” Computer Networks and ISDN Systems, Vol. 28, No. 7-11, 1996, pp. 1007-1014.
[7] R. Baraglia and F. Silvestri, “An Online Recommender System for Large Web Sites,” Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, Beijing, 20-24 September 2004.
[8] Y. M. AlMurtadha, M. N. B. Sulaiman, N. Mustapha and N. I. Udzir, “Mining Web Navigation Profiles for Recommendation System,” Information Technology Journal, Vol. 9, 2010, pp. 790-796. doi:10.3923/itj.2010.790.796
[9] T. H. Cormen, C. E. Leiserson and R. L. Rivest, “Introduction to Algorithms,” MIT Press, Cambridge, 1990.
[10] D. S. Hirschberg, “Algorithms for the Longest Common Subsequence Problem,” Journal of the ACM, Vol. 24, No. 4, 1977, pp. 664-675. doi:10.1145/322033.322044

  
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