Efficient mining of both positive and negative association rules

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 <i>A</i> ⇒ ¬ <i>B</i>, ¬ <i>A</i> ⇒ <i>B</i>, and ¬ <i>A</i> ⇒ ¬ <i>B</i>, 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 effectiveness and efficiency.

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