This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬B, ¬A ⇒ B, and ¬A ⇒ ¬B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its

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... eness and efficiency. X. Wu et al. can be used to predict that 'if A occurs in a transaction, then B will likely also occur in the same transaction', and we can apply this association rule to place 'B close to A' in the store layout and product placement of supermarket management. Such applications are expected to increase product sales and provide more convenience for supermarket customers. Therefore, mining association rules in databases has received much attention recently [Aggarawal