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<p><b>Abstract</b>—Many sequential algorithms have been proposed for mining of association rules. However, very little work has been done in mining association rules in distributed databases. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. In this study, an efficient algorithm, DMA, is proposed. It generates a small number of candidate sets and requires only <it>O</it>(<it>n</it>) messages for support count exchange for each candidate set, where <it>n</it> is the number of sites in a distributed database. The algorithm has been implemented on an experimental test bed and its performance is studied. The results show that DMA has superior performance when comparing with the direct application of a popular sequential algorithm in distributed databases.</p>
Data mining, knowledge discovery, distributed data mining, association rule, distributed database, distributed algorithm, partitioned database.

A. W. Fu, D. W. Cheung, Y. Fu and V. T. Ng, "Efficient Mining of Association Rules in Distributed Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 911-922, 1996.
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