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Reseach Article

A New Algorithm for Web Log Mining

by Gajendra Singh, Priyanka Dixit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 17
Year of Publication: 2014
Authors: Gajendra Singh, Priyanka Dixit
10.5120/15812-4652

Gajendra Singh, Priyanka Dixit . A New Algorithm for Web Log Mining. International Journal of Computer Applications. 90, 17 ( March 2014), 20-24. DOI=10.5120/15812-4652

@article{ 10.5120/15812-4652,
author = { Gajendra Singh, Priyanka Dixit },
title = { A New Algorithm for Web Log Mining },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 17 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number17/15812-4652/ },
doi = { 10.5120/15812-4652 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:11:17.402252+05:30
%A Gajendra Singh
%A Priyanka Dixit
%T A New Algorithm for Web Log Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 17
%P 20-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The enormous content of information on the World Wide Web makes it obvious candidate for data mining research. Data Mining Technique application is used to the World Wide Web referred as Web mining where this term has been used in three distinct ways; , Web Structure Mining, Web Content Mining and Web Usage Mining. Web Log Mining is one of the Web based application where it will facing with large amount of log data. In order to produce the web log through portal usage patterns and user behaviors, this research work implements the high level process of Web Log mining technique using basic rules. Web Log Mining consists of three main phases, namely Data Preprocessing, Pattern filtering and Pattern Analysis. As we know that server log files become a set of raw data where it's must go through with all the Web Log Mining phases to producing the final results. Here, Web Log mining, approach has been combining with the basic rules, to optimize the total execution time. Finally, this work wills present an overview of results analysis and Web Log Mining can use the findings for the suitable valuable actions.

References
  1. K. Sudheer Reddy, G. Partha Saradhi Varma and S. Sai Satyanarayana Reddy Understanding the Scope of Web Usage Mining & Applications of Web Data Usage PatternsIEEE International Conference 2012
  2. RuPeng Luan*, SuFen Sun, JunFeng Zhang, Feng Yu, Qian Zhang A Dynamic Improved Apriori Algorithm and Its Experiments in Web Log Mining 9th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012) 2012
  3. Mahendra Pratap Yadav, Pankaj Kumar Keserwani and Shefalika Ghosh Samaddar An Efficient Web Mining Algorithm for Web Log Analysis: E-Web Miner 1st IEEE Int'l Conf. on Recent Advances in Information Technology | RAIT-2012 |
  4. Indrajit Mukherjee, V. Bhattacharya, Samudra Banerjee, Pradeep Kumar Gupta and P. K. Mahanti Efficient Web Information Retrieval based on U sage Mining ]. 1 Infl Conf. on Recent Advances in Information Technology I RAIT-20121
  5. S. Balaji and S. Sarumathi TOPCRAWL: Community Mining in Web search Engines with emphasize on Topical crawling Proceedings of the IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering, March 21-23, 2012
  6. K. R. Suneetha, Dr. R. Krishnamoorthi- "Identifying User Behavior by Analyzing Web Server Access Log File", IJCSNS International Journal of Computer Science and Network Security, VOL. 9 No. 4, April 2009
  7. Mark E. Snyder, Ravi Sundaram, Mayur Thakur- "Preprocessing DNS Log Data for Effective Data Mining", 2008.
  8. S. Sun and J. Zambreno, "Mining Association Rules with Systolic Trees," Proc. In!'1 Conf Field-Programmable Logic and Applications (FPL '08), Sept 2008.
  9. G. Stumme, A. Ho tho, and B. Berendt. Semantic web mining: State of the art and future directions. Journal of Web Semantics: Science, Services and Agents on the World WideWeb, 4(2):124–143, 2006.
  10. R. R. Sarukkai. Link prediction and path analysis using markov chains. In Proceedings of the 9th Intl. World Wide Web Conf. (WWW'00), pages 377–386, 2000.
  11. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the 5th Int'l Conference on Extending Database Technology: Advances in Database Technology, pages 3–17, 1996
  12. Personalized Web Search by Mapping User Queries to Categories- Fang Liu Clement Yu Weiyi Meng Department of Computer Science, Department of Computer Science, Department of Computer Science, University of Illinois at Chicago University of Illinois at Chicago SUNY at Binghamton Chicago.
Index Terms

Computer Science
Information Sciences

Keywords

Server Log File Data Mining Web Mining Web Log Mining Association Rules Apriori Algorithm.