• Ng Qi Yau School of Computer Sciences, UniversitiSains Malaysia, 11800, Penang, Malaysia
  • Wan Mohd Nazmee Wan Zainon School of Computer Sciences, UniversitiSains Malaysia, 11800, Penang, Malaysia




Web Usage Mining, Data Mining Algorithms, Mining Techniques and Pattern Discovery


Web Usage Mining is a computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis and database systems with the goal to extract valuable information from accessing server logs of World Wide Web data repositories and transform it into an understandable structure for further understanding and use. Main focus of this paper will be centered on exploring methods that expedites the log mining process and present the result of log mining process through data visualization and compare data-mining algorithms. For the comparison between classification techniques, precision, recall and ROC area are the correct measures that are used to compare algorithms. Based on this study it shows that Naïve Bayes and Bayes Network are proven to be the best algorithms for that.


Download data is not yet available.


Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N. (2002): Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1, pp. 12-23.

Jose M. Domenech, Javier Lorenzo (2007). A Tool for Web Usage Mining. 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'07), Birmingham, UK. DOI: https://doi.org/10.1007/978-3-540-77226-2_70

Anand S. Lalani (2003). Data Mining of Web Access Logs. A minor thesis, School of Computer Science and Information Technology, Faculty of Applied Science, Royal Melbourne Institute of Technology, Melbourne, Victoria, Australia.

James Pitkow (1997). In search of reliable usage data on the www. In Proc. of the Sixth International WWW Conference, pp. 1-13. DOI: https://doi.org/10.1016/S0169-7552(97)00021-4

Teressa T. Chikohora (2014): A Study of the Factors Considered when Choosing an Appropriate Data Mining Algorithm. International Journal of Soft Computing and Engineering (IJSCE) , Volume-4, Issue-3

Berson, A., Smith, S., Thearling, K. (ed.) (1999). Building Data Mining Applications for CRM. NewYork: McGraw-Hill.

Aniket Dash (2010) Web Usage Mining: An Implementation. A minor thesis, National Institute of Technology, Rourkela, India.

Robert Cooley, BamshadMobasher, and Jaideep Srivastava (1999). Data Preparation for Mining World Wide Web. Browsing Patterns Knowledge and Information Systems Vol. 1 Issue 1, pp 5- 32. DOI: https://doi.org/10.1007/BF03325089

Spilipoulou M., Mobasher B, Berendt B. (2003) “A framework for the Evaluation of Session Reconstruction Heuristics in Web Usage Analysis,” INFORMS Journal on Computing spring. DOI: https://doi.org/10.1287/ijoc.

ChitraNasa, Suman (2012). Evaluation of Different Classification Techniques for WEB Data. International Journal of Computer Applications (0975–8887), Volume 52.




How to Cite

Yau, N. Q., & Zainon, W. (2017). UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES . International Journal of Engineering Technologies and Management Research, 4(9), 18–26. https://doi.org/10.29121/ijetmr.v4.i9.2017.96