Discovering User Behaviour Patterns from Web Log Analysis to Enhance the Accessibility and Usability of Website
Authors: Harpreet Singh
Finding relevant information on the World Wide Web is becoming highly challenging day by day. Web usage mining is used for the extraction of relevant and useful knowledge, such as user behaviour patterns, from web access log records. Web access log records all the requests for individual files that the users have requested from the website. Web usage mining is important for Customer Relationship Management (CRM), as it can ensure customer satisfaction as far as the interaction between the customer and the organization is concerned. Web usage mining is helpful in improving website structure or design as per the user’s requirement by analyzing the access log file of a website through a log analyzer tool. The focus of this paper is to enhance the accessibility and usability of a guitar selling web site by analyzing their access log through Deep Log Analyzer tool. The results show that the maximum number of users is from the United States and that they use Opera 9.8 web browser and the Windows XP operating system.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131723Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 628
 Arvind K. Sharma, P. C. Gupta “Identifying the Number of Visitors to improve Website Usability from Educational Institution Web Log Data”, International Journal of Computer Applications Technology and Research, Vol. 2, Issue 1, pp 22-26, 2013.
 L. K. Joshila Grace, V. Maheswari, Dhinaharan Nagamalai, “Analysis of Web Logs and Web User in Web Mining”, International Journal of Network Security & its applications, Vol.3, No.1, January 2011.
 Navjot Kaur, Himanshu Aggarwal, “A Comparative Study of WUM tools to Analyze User Behaviours Pattern from Web Log Data” International Journal of Advances in Engineering Research, Vol. No. 10, Issue No. VI, December 2015.
 Cooley, R., “Web Usage Mining: Discovery and Application of Interesting Patterns from Web data”, 2000, http://citeseer.nj.nec.com/426030.html.
 Navjot Kaur, Himanshu Aggarwal, “A Novel Semantically-Time-Referrer based Approach of Web Usage Mining for Improved Sessionization in Pre-processing of Web Log” , International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, Feb 2017
 G. Castellano, A. M. Fanelli, M. A. Trsello, “Log data preparation for mining Web usage patterns” IADIS International Conference Applied Computing, pp 371-378, 2007.
 Doru Tanasa, Brigitte Trousse, “Advanced Data Preprocessing for Intersites Web Usage Mining” Enhancing information, IEEE Intelligent System, pp 59-65,2004.
 Aswin G. Raiyani, Sheetal S. Pandya, “Discovering User Identification Mining Technique for Preprocessing Log Data”, ISSN: 0975 – 6760, Vol. 2, Issue 2, pp 477-482, Nov 12 to Oct 13.
 Navjot Kaur, Himanshu Aggarwal, “Web Log Analysis for Identifying the Number of Visitors and their Behavior to Enhance the Accessibility and Usability of Website”, International Journal of Computer Applications (0975 – 8887), Vol. 110, No. 4, January 2015.
 Yogish H K, G T Raju, Manjunath T N, “The Descriptive Study of Knowledge Discovery from Web Usage Mining”, International Journal of Computer Science, Vol. 8, Issue 5, No 1, Sep 2011.
 Statcounter, Retrieved data from: https://statcounter.com ON 20 Feb 2017.
 Deep Log Analyzer, Retrieved data from: “https://www.deep-software.com/features/” on 20 Feb 2017.
 Web Log Expert Lite, Retrieved data from: http://www.weblogexpert.com on 20 Feb 2017.