Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse

Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse

Yun Sing Koh (University of Otago, New Zealand), Nathan Rountree (University of Otago, New Zealand) and Richard O’Keefe (University of Otago, New Zealand)
DOI: 10.4018/978-1-59904-951-9.ch204
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Discovering association rules efficiently is an important data mining problem. 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. To alleviate this problem, we propose a new method of discovering sporadic rules without having to produce all other rules above the minimum support threshold. The new method, called Apriori-Inverse, is a variation of the Apriori algorithm that uses the notion of maximum support instead of minimum support to generate candidate itemsets. Candidate itemsets of interest to us fall below a maximum support value but above a minimum absolute support value. Rules above maximum support are considered frequent rules, which are of no interest to us, whereas rules that occur by chance fall below the minimum absolute support value. 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). This article is an expanded version of Koh and Rountree (2005).

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