Extraction of Knowledge from Web Server Logs Using Web Usage Mining
Keywords:Web Mining, Web Usage Mining, Extraction of Knowledge, Web Server Logs, Clustering, Web Log Pre-Processing
Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.
J. Srivastava, P. Desikan and V. Kumar, “Web Mining: Accomplishments and Future Directions”, Proc. USNatl Science Foundation Workshop on Next-generation data mining (NGDM) Nat Science Foundation, 2002.
Tawfiq A. Al-Asdi and Ahmed J Obaid, “An Efficient Web Usage Mining Algorithm Based on Log File Data”, Journal of Theoretical and Applied Information Technology, Vol. 92, No.2, 2016, pp. 215-223
R. Kosala and H. Blockeel, “Web Mining Research: A Survey”, ACM SIGKDD explorations, Vol. 2, No.1, pp. 1-15, 2000.
AmitPratap Singh and Dr. R.C. Jain, “ A Survey on different phases of Web Usage Mining for Anomaly user behaviour Investigation”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Vol. 3, No. 3, May-June 2014.
R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance improvements”, in 5thInternational Conference Extending Database Technology, AVignon, France, pp.13-17, March 1996.
C. Gomathi and M. Moorthi, “Web Access Pattern Algorithms in Education Domain” Computer and Information Science Journal, Vol.1, No.4, Nov. 2008.
Mr. Dushyant, B. Rathod, and Dr. SamratKhanna, “A Review on Emerging Trends of Web Mining and its Applications”, ISSN: 2321-9939.
Dr. S. Vijiyarani and E. Suganya, “Research issues in Web Mining”, International Journal of Computer Aided Technologies (IJCAX), Vol.2, No.3, July 2015.
Vijayashri Losarwar and Dr. Madhuri Joshi, “Data Pre-processing in Web Usage Mining”, International Conference on Artificial Intelligence and Embedded Systems (ICAIES’2012) July 15-16, Singapore 2012.
Sheetal A. Raiyani and Shailendra Jain, “Efficient Pre-processing Technique using Web log mining”, International Journal of Advancements in Research & Technology”, Vol. 1, No. 6, 2012.
Manisha Valera and Kirit Rathod, “A Novel Approach of Mining Frequent Sequential Pattern from Customized Web Log Pre-processing”, International Journal of Engineering Research Applications (IJERA), Vol. 3, No. 1, pp. 269-380, Jan-Feb 2013.
K. Sudheer Reddy, G. ParthaSaradhi Varma, and M. Kantha Reddy, “An Effective Pre-processing Method for Web Usage Mining”, International Journal of Computer Theory and Engineering, Vol. 6, No.5, October 2014.
Amandeep Kaur Mann and Navneet Kaur, “Survey Paper on Clustering Techniques”, International Journal of Science Engineering and Technology Research (IJSETR), Vol. 2, No. 4, Apr 2013.
J. Srivastava, Robert Cooley, M. Deshpande and Pang-Ning Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, SIGKDD Explorations ACM SIGKDD, Vol. 1, No. 2, Jan 2000
R. Suguna, and Dr. D. Sharmila, “User Interest Level Based Pre-processing Algorithms using Web Usage Mining”, IJCSE, Vol. 5, No. 9, Sep. 2013.