TagClus: a random walk-based method for tag clustering

Tagging behavior on the Internet has seen dramatic increase in recent years, and social tagging has become a popular way to organize and share resources. However, ambiguity and large quantities of tags restrict its effective use for resource searching and classifying. Tag clustering can group tags with similar semantics together, thus helping alleviate these problems. In this paper, we introduce a random walk-based method to measure relevance between tags by exploiting the relationship between tags and resources. Based on this, we also develop a novel clustering method, TagClus, which can address several challenges in tag clustering. Experimental results on a real dataset show that our methods achieve good accuracy and acceptable performance for tag clustering.