Using Association Rules for Discovering Tag Bundles in Social Tagging Data

Social tagging is one of the major phenomenatransforming the World Wide Web(WWW) from a staticplatform into an actively shared information space. Social bookmarking tools allow users to save for further use, creating tags for annotating Internet websites, and to share Web pages they found interesting with other people. This project presents a research study on the application of link mining to a social bookmarking Web site called del.icio.us. User feedback is obtained by iteratively providing a set of posted items, and users selecting items based on their own preferences either using this suggestion set or from the set of all possible items. To improve existing social bookmarking systems and to design new ones, researchers and practitioners need to understand how to evaluate tagging behavior.Existing approaches suffers with We investigated the user bookmarking and tagging behaviors and seen several approaches to find surprising patterns in the data.Proposed system uses robust dynamic tagging data with popular tag and na ve algorithms in order to overcome the drawbacks in the existing algorithms. This study mainly focuses on processing popular than others. Finally this system effectively resolved the tags for mutual ties between users