Mining Positive and Negative Association Rules from Large Databases

C. Cornelis, Peng Yan, Xing Zhang, Guoqing Chen
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引用次数: 72

Abstract

This paper is concerned with discovering positive and negative association rules, a problem which has been addressed by various authors from different angles, but for which no fully satisfactory solution has yet been proposed. We catalogue and critically examine the existing definitions and approaches, and we present an a priori-based algorithm that is able to find all valid positive and negative association rules in a support-confidence framework. Efficiency is guaranteed by exploiting an upward closure property that holds for the support of negative association rules under our definition of validity
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从大型数据库中挖掘正、负关联规则
本文研究的是正关联规则和负关联规则的发现问题,这一问题已经被许多作者从不同的角度提出,但至今还没有一个完全令人满意的解决方案。我们对现有的定义和方法进行了编目和批判性检查,并提出了一种基于优先级的算法,该算法能够在支持-置信度框架中找到所有有效的正面和负面关联规则。在我们的有效性定义下,效率是通过利用一个向上的闭包特性来保证的,该闭包特性支持负关联规则
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