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User behavior analysis has played an important role in Web information retrieval. Rare queries, whose frequencies are rather low, are usually ignored in existing studies due to the data sparseness. Little has been known about the mass of rare queries on either the information need or the user behavior. In this paper, we make an empirical study of users' behavior on rare queries using a large scale search log. Features concerning query, resource and post-query actions are analyzed, based on which we propose a practical categorization framework and obtain an overview of rare query composition. Further, we study the characteristics of several most commonly occurring types of rare queries, and suggest improving the search performance of them separately. This work gives more insights into understanding the long tail of queries and will be helpful for Web search in terms of rare queries.