In this paper we consider concurrent execution of multiple data mining queries in the context of discovery of frequent itemsets. If such data mining queries operate on similar parts of the database, then their overall I/O cost can be reduced by transforming the set of data mining queries into another set of non-overlapping queries, whose results can be used to efficiently answer the original queries. We discuss the problem of multiple data mining query optimization and experimentally evaluate the Mine Merge algorithm to efficiently execute sets of data mining queries.