文章基本信息

标题：An Efficient Approach to Prune Mined Association Rules in Large Databases
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作者：D. Narmadha ; G. NaveenSundar ; S. Geetha 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：1
出版社：IJCSI Press
摘要：Association rule mining finds interesting associations and/or correlation relationships among large set of data items. However, when the number of association rules become large, it becomes less interesting to the user. It is crucial to help the decision-maker with an efficient postprocessing step in order to select interesting association rules throughout huge volumes of discovered rules. This motivates the need for association analysis. Thus, this paper presents a novel approach to prune mined association rules in large databases. Further, an analysis of different association rule mining techniques for market basket analysis, highlighting strengths of different association rule mining techniques are also discussed. We want to point out potential pitfalls as well as challenging issues need to be addressed by an association rule mining technique. We believe that the results of this approach will help decision maker for making important decisions.
关键词：CLOSET; MAFIA; FP; Ontology; User constraint