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The problem of indices selection on the large size data warehouses affects the efficiency of the query processing of the data warehouses. The lower query processing costs can be obtained by using indices, but the indices occupy the large storage areas and induce the index maintenance costs which are accompanied by database updates. By using the bitmap join indices, we can optimize the star join queries which join a fact table and many dimension tables, and do the selection on dimension tables in data warehouses. Although the bitmap join index takes the lower storage costs, the task to select the indexing attributes among the huge candidate attributes is difficult. In this paper we reduce the number of candidate attributes for bitmap join index by the data mining techniques. Compared to the existing techniques which reduce the number of candidate attributes by the frequencies of attributes, we consider the frequencies of attributes and the size of dimmension tables and the size of the tuples of the dimension tables and the page size of disk. We reduce the great number of candidate attributes by using the frequent itemsets mining techniques. We make the bitmap join indices which have the least costs and the least storage areas adapted to storage constraints by using the cost functions applied to the bitmap join indices of the candidate attributes. We compare the existing techniques and ours, and analyze them in order to evaluate the efficiencies of ours.