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论文类型：期刊论文

发表时间：2013-01-01

发表刊物：ICIC Express Letters, Part B: Applications

收录刊物：EI、Scopus

卷号：4

期号：4

页面范围：913-920

ISSN号：21852766

摘要：This paper discusses and analyzes the measure of Support-Confidence as well as its limitation, and proposes a novel interestingness measure to improve the quality of the rules discovered from association rule mining. The proposed measure employs both objective measure and subjective measure to facilitate pruning rules. Besides, this interestingness measure incorporates context information into the process of rules filtering. To validate the feasibility of our measure, experiments are carried out on three different datasets. Experimental results show that the improved interestingness measure significantly reduces the number of redundant rules. Thus, the proposed measure outperforms the framework of Support-Confidence in term of effectiveness. And it has the advantages of reducing the creation of redundant rules and low-relation rules under the mobile ecommerce environment where contexts are critical to the success of the application. ? 2013 ISSN 2185-2766.