Finding Sporadic Rules Using Apriori-Inverse

We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). We show that Apriori-Inverse finds all perfectly sporadic rules much more quickly than Apriori. We also propose extensions to Apriori-Inverse to allow us to find some (but not necessarily all) imperfectly sporadic rules.