Open Access   Article

Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach

A. Saravanan1 , S. Sathya Bama2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 21-26, Feb-2019


Online published on Feb 28, 2019

Copyright © A. Saravanan, S. Sathya Bama . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. Saravanan, S. Sathya Bama, “Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.21-26, 2019.

MLA Style Citation: A. Saravanan, S. Sathya Bama "Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach." International Journal of Computer Sciences and Engineering 7.2 (2019): 21-26.

APA Style Citation: A. Saravanan, S. Sathya Bama, (2019). Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach. International Journal of Computer Sciences and Engineering, 7(2), 21-26.

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With nurturing reputation of the World Wide Web, large quantity of web usage data is collected by the web servers and stored in web access log files. Web usage mining is a technology to mine valuable knowledge from the World Wide Web. It intends to discover interesting user access patterns from web log files. Analysis of these user access patterns is used to determine that the information architecture of the web site can be reorganized to better facilitate information retrieval. Association rule mining is also used to find association relationships amongst large data sets. Mining frequent sequential patterns is a significant aspect in association rule mining. Based on this, the changes can be suggested to the web site by placing embedded hyperlinks on the home page to the frequently accessed sections of the web site. In this paper, a very efficient algorithm has been adopted for expert systems to mine frequent sequential patterns in web usage data

Key-Words / Index Term

Data Mining, Web Mining, Association rule mining, Frequent sequential pattern mining, Web Log files, Web usage


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