Mining interesting imperfectly sporadic rules

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 number 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. There are two types of sporadic rules: perfectly sporadic and imperfectly sporadic. Here we are more concerned about finding imperfectly sporadic rules, where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. In this paper, we introduce an algorithm called Mining Interesting Imperfectly Sporadic Rules (MIISR) to find imperfectly sporadic rules efficiently, e.g. fever, headache, stiff neck → meningitis. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time. This paper is an expanded version of Koh et al. [Advances in knowledge discovery and data mining: 10th Pacific-Asia Conference (PAKDD 2006), Singapore.