A Neighborhood Search Method for Link-Based Tag Clustering

Authors:
Jianwei Cui;Pei Li;Hongyan Liu;Jun He;Xiaoyong Du
Affiliations:
Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing 100872;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing 100872;Department of Management Science and Engineering, Tsinghua University, Beijing 100084;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing 100872;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing 100872
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

Recently tagging has been a flexible and important way to share and categorize web resources. However, ambiguity and large quantities of tags restrict its value for resource sharing and navigation. Tag clustering could help alleviate these problems by gathering relevant tags. In this paper, we introduce a link-based method to measure the relevance between tags based on random walk on graphs. We also propose a new clustering method which could address several challenges in tag clustering. The experimental results based on del.icio.us show that our methods achieve good accuracy and acceptable performance on tag clustering.