Abstract

Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to "push" support constraints into the Apriori itemset generation so that the "best" minimum support is used for each itemset at run time to preserve the essence of Apriori. 1 Introduction The association rules mining, first studied in [AIS93, AS94] for market-basket analysis, is to find all association rules above some user-specified minimum support and minimum confidence. The bottleneck of this problem is finding frequent itemsets (and supp...

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