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Many large organizations have multiple large databases as they transact from multiple branches. Most of the previous pieces of work are based on a single database. Thus, it is necessary to study data mining on multiple databases. In this paper, we propose two measures of similarity between a pair of databases. Also, we propose an algorithm for clustering a set of databases. Efficiency of the clustering process has been improved using the following strategies: reducing execution time of clustering algorithm, using more appropriate similarity measure, and storing frequent itemsets space efficiently.