Mining Frequent Itemsets Using Support Constraints

Abstract

Interesting patterns often occur at varied lev-els of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suers 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 frame-work of frequent itemset mining in the pres-ence of support constraints. Our approach is to \push " support constraints into the Apriori itemset generation so that the \best " mini-mum support is used for each itemset at run time to preserve the essence of Apriori.