Efficient Web Log Mining using Doubly Linked Tree

Details Efficient Web Log Mining using Doubly Linked Tree Efficient Web Log Mining using Doubly Linked Tree

2009-07-30

arxiv.org/abs/0907.5433v1

International Journal of Computer Science and Information Security, IJCSIS, July 2009, Volume 3. No.1, USA

Computer Science

Information Retrieval

5 pages, International Journal of Computer Science and Information Security, ISSN 1947 5500, Impact Factor 0.423

Scientific paper

World Wide Web is a huge data repository and is growing with the explosive rate of about 1 million pages a day. As the information available on World Wide Web is growing the usage of the web sites is also growing. Web log records each access of the web page and number of entries in the web logs is increasing rapidly. These web logs, when mined properly can provide useful information for decision-making. The designer of the web site, analyst and management executives are interested in extracting this hidden information from web logs for decision making. Web access pattern, which is the frequently used sequence of accesses, is one of the important information that can be mined from the web logs. This information can be used to gather business intelligence to improve sales and advertisement, personalization for a user, to analyze system performance and to improve the web site organization. There exist many techniques to mine access patterns from the web logs. This paper describes the powerful algorithm that mines the web logs efficiently. Proposed algorithm firstly converts the web access data available in a special doubly linked tree. Each access is called an event. This tree keeps the critical mining related information in very compressed form based on the frequent event count. Proposed recursive algorithm uses this tree to efficiently find all access patterns that satisfy user specified criteria. To prove that our algorithm is efficient from the other GSP (Generalized Sequential Pattern) algorithms we have done experimental studies on sample data.

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