Off late there has been massive growth in the use of tags as a simple, flexible way to categorize resources. Tags are often used collaboratively to help share information using website; such as del.icio.us. However, the number of tags used in such a service is extremely large, so the unstructured nature of tags limits their value when navigating these websites, and prevents users from fully exploiting tags added by others. Clustering similar tags can improve this by adding structure. This paper discusses techniques for deriving tag similarity and explains two tag clustering algorithms. The paper applies the algorithms to two datasets containing tags provided by users with common interests.
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