期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：5
页码：4976-4980
出版社：TechScience Publications
摘要：Social bookmarking tools enable users to save URLs for future reference, to create tags for annotating Web pages, and to share Web pages they found interesting with others. This project presents a case study on the application of link mining to a social bookmarking Web site called del.icio.us. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. This proposed system investigates the user bookmarking and tagging behaviors by applying robust Naïve bayes, Association rule mining techniques to find surprising patterns in the data. This study mainly focuses on predicting popular rules than others. Finally, we demonstrated the effectiveness of using data mining algorithms for predicting effective tags rules behavior for delicious web data.
