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摘要： 基于支持度的关联规则挖掘算法无法找到那些非频繁但效用很高的项集，基于效用的关联规则会漏掉那些效用不高但发生比较频繁、支持度和效用值的积（激励）很大的项集。提出了基于激励的关联规则挖掘问题及一种自下而上的挖掘算法HM-miner。激励综合了支持度与效用的优点，能同时度量项集的统计重要性和语义重要性。HM-miner利用激励的上界特性进行减枝，能有效挖掘高激励项集。
