Similarity searches are indispensible to string data because string data often contain errors by nature. Many techniques for string similarity searches are based on merging inverted lists. In this paper, we propose a clustering technique for string data by exploiting the distribution and frequency of strings in inverted lists. We also propose a partitioned inverted index to effectively retrieve a group inverted lists from clusters. Experimental results show that the proposed technique improves existing algorithms significantly.