Email this article Easing Operative User Steering through Website Construction Development
Abstract
Data Mining is a step of Knowledge Discovery in Databases. Clustering can be considered the most important unsupervised learning technique so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data and mining major issue is removing unrelavent data. But here we did not find these type data its fail on removing irrelevant and remove back tracking. For this type of problems Designing well-Construction websites to facilitate Operative user Steering has long been a challenge. While various methods have been proposed to re link web pages to improve navigability using user Steering data, the completely reorganized new Construction can be highly unpredictable, and the cost of disorienting users after the changes remains unanalyzed. This paper addresses how to improve a website without introducing substantial changes. By analyzing the efficiency of the proposed work and the existing work, the time taken to retrieve the data will be better in the proposed by removing all the irrelevant features which are gets analyzed. The experimental results are better than 18% on removing irrelevant and 26% for back tracks it is better solution for previous methods.
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References
Wenpu Xing, Ghorbani , Weighted page rank algorithm, A communication networks and services Research,Proceedin g. Second Annual Conference 2004 .
M. Perkowitz and O.Etzioni, Towards adaptive web Sites Conceptual Framework and Case Study, Artificial Intelligence, vol. 118, pp. 245-275,2000.
Nakagawa and Mobasher, A hybrid web personalization Model Based onSite Connectivity, Proc.Web Knowledge Discovery Data Mining Workshop, pp59-70, 2003.
C.C.Lin, Optimal Web Site Recognization Considering Information Overload and Reserch Depth ,EuropeanJ. Operational Research, Vol.173,no.3,pp 839-848 , 2006
W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal, “From User Access Patterns to Dynamic Hypertext Linking,” Computer Networks and ISDN Systems, vol. 28, nos. 7-11, pp. 1007-1014, May 1996.
John Eighmey. Profiling user responses tocommercial web sites. Journal of Advertising Research, 37(2):59–66,May-June 1997.
Paul Alpar. Satisfaction with a web site. In August-Wilhelm Scheer and Markus Nüttgens, editors,4. Internationale Tagung Wirtschafts informatik 1999. PhysicaVerlag, Heidelberg, 1999.
Y. Fu, M.Y. Shih, M. Creado, and C. Ju, “Reorganizing Web Sites Based on User Access Patterns,” Intelligent Systems in Accounting, Finance and Management, vol. 11, no. 1, pp. 39-53, 2002.
P. Pirolli and S.K. Card, “Information Foraging,” Psychological Rev., vol. 106, no. 4, pp. 643-675, 1999.
R. Gupta, A. Bagchi, and S. Sarkar, “Improving Linkage of Web Pages,” INFORMS J. Computing, vol. 19, no. 1, pp. 127-136, 2007.
R. Srikant and Y. Yang, “Mining Web Logs to Improve Web Site Organization,” Proc. 10th Int’l Conf. World Wide Web, pp. 430-437, 2001.
C.C. Lin and L. Tseng, “Website Reorganization Using an Ant Colony System,” Expert Systems with Applications, vol. 37, no. 12, pp. 7598-7605, 2010.
T. Boutell, “WWW FAQs: How Many Websites AreThere?”http://www.boutell.com/newfaq/misc/sizeofweb.html, 2007.
R. Gupta, A. Bagchi, and S. Sarkar, “Improving Linkage of Web Pages,” INFORMS J. Computing, vol. 19, no. 1, pp. 127-136, 2007.
M. Eirinaki and M. Vazirgiannis, “Web Mining for Web Personalization,” ACM Trans. Internet Technology, vol. 3, no. 1,pp. 1-27, 2003.
B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, “Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization, “Data Mining and Knowledge Discovery, vol. 6, no. 1, pp. 61-82, 2002.
B. Mobasher, R. Cooley, and J. Srivastava, “Automatic PersonalizationBased on Web Usage Mining,” Comm. ACM, vol. 43, no. 8,pp. 142-151, 2000.
B. Mobasher, R. Cooley, and J. Srivastava, “Creating Adaptive Web Sites through Usage-Based Clustering of URLs,” Proc.Workshop Knowledge and Data Eng. Exchange, 1999.
W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal, “From UserAccess Patterns to Dynamic Hypertext Linking,” Computer Networksand ISDN Systems, vol. 28, nos. 7-11, pp. 1007-1014, May 1996.
B. Mobasher, “Data Mining for Personalization,” The Adaptive Web: Methods and Strategies of Web Personalization, A. Kobsa, W. Nejdl, P. Brusilovsky, eds., vol. 4321, pp. 90-135, Springer-Verlag,2007.
Y. Yang, Y. Cao, Z. Nie, J. Zhou, and J. Wen, “Closing the Loop inWebpage Understanding,” IEEE Trans. Knowledge and Data Eng.,vol. 22, no. 5, pp. 639-650, May 2010.
J. Hou and Y. Zhang, “Effectively Finding Relevant Web Pages from Linkage Information,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 4, pp. 940-951, July/Aug. 2003.
H. Kao, J. Ho, and M. Chen, “WISDOM: Web Intrapage Informative Structure Mining Based on Document Object Model,”IEEE Trans. Knowledge and Data Eng., vol. 17, no. 5, pp. 614-627,May 2005.
H. Kao, S. Lin, J. Ho, and M. Chen, “Mining Web Informative Structures and Contents Based on Entropy Analysis,” IEEETrans. Knowledge and Data Eng., vol. 16, no. 1, pp. 41-55, Jan. 2004
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