Abstract
Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large amount of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. In this paper we introduce an algorithm called MIISR to find a particular type of sporadic rule efficiently: where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time.
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