Mining for association rules is considered an important data mining problem. Many different variations of this problem have been described in the literature. We introduce the problem of mining for negative associations. A naive approach to finding negative associations leads to a very large number of rules with low interest measures. We address this problem by combining previously discovered positive associations with domain knowledge to constrain the search space such that fewer but more interesting negative rules are mined. We describe an algorithm that efficiently finds all such negative associations and present the experimental results.
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