Efficient Remining of Generalized Association Rules Under Multiple Minimum Support Refinement

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examine the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is refined and propose a novel algorithm called RGA_MSR to accomplish the work. By keeping and utilizing the set of frequent itemsets and negative border, and adopting vertical intersection counting strategy, the proposed RGA_MSR algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and has very good performance.