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Strong negative association rules can reveal irrelevances hidden between frequent itemsets. Existing research has made significant efforts in discovering both positive and negative association rules from single database. This paper presents an efficient method for mining strong negative association rules in multi-database. The method produces some strong negative relational patterns (a kind of infrequent itemsets) by pruning and scanning constructed multi-database frequent pattern tree, and extracts strong negative association rules according to the proposed correlation model. The experimental results show the effectiveness and efficiency of the proposed algorithm.