期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2013
卷号：3
期号：2
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：World Wide Web plays a significant role in human life. It requires a technological improvement to satisfy the user needs. Web log data is essential for improving the performance of the web. It contains large, heterogeneous and diverse data. Analyzing g the web log data is a tedious process for Web developers, Web designers, technologists and end users. In this work, a new weighted association mining algorithm is developed to identify the best association rules that are useful for web site restructuring and recommendation that reduces false visit and improve users’ navigation behavior. The algorithm finds the frequent item set from a large uncertain database. Frequent scanning of database in each time is the problem with the existing algorithms which leads to complex output set and time consuming process. The proposed algorithm scans the database only once at the beginning of the process and the generated frequent item sets, which are stored into the database. The evaluation parameters such as support, confidence, lift and number of rules are considered to analyze the performance of proposed algorithm and traditional association mining algorithm. The new algorithm produced best result that helps the developer to restructure their website in a way to meet the requirements of the end user within short time span.
关键词：Web usage mining; Web page prediction; Dynamic Programming; Apriori; Weighted Association Rule;Mining (WARM).
