Abstract
usage mining is about analyzing the user interactions with a web server based on resources like web logs, click streams and database transactions. It helps in discovering the browsing patterns of the user and in relating the pages visited by him. This knowledge can be of help in making business decisions, refining the web site design to derive personalized pages. Web usage mining uses the data mining and the incremental mining techniques to extract the user usage patterns and study the visiting characteristics of the user. Normally, the source for all this data is the web server log, which changes dynamically. Conventional data mining techniques were proposed to be inefficient, as they need to be re-executed every time the log changes which in turn requires multiple database scans. This paper discusses an algorithm to suit the dynamically changing log scenario, based on association rule mining with incremental technique. The algorithm proved to be more efficient as there are redundant no of scans of database; it avoids the generation of candidate keys and allows interactive mining with different supports. The experimental results proving this claim are given in this paper.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
