Association rules and decision rules

A. Mokkadem, M. Pelletier, Louis Raimbault
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Abstract

Determining association rules of significant interest is an essential task within data mining and statistical analysis. In this paper, we first precisely define the notion of association rule. For this, we introduce a general model, which includes the usual transaction model, and which allows many operations on the association rules. Then, we interpret association rules as statistical decision rules. This interpretation leads to four decisional measures, one of them being the usual confidence. Then, we give some strategies based on the use of these four decisional measures in order to select or to construct association rules with a given consequent. We finally present an experimental study to illustrate these strategies. This study is carried out in R language, with the R‐package we specifically built for association rules mining.
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关联规则和决策规则
确定重要的关联规则是数据挖掘和统计分析中的一项基本任务。本文首先对关联规则的概念进行了精确的定义。为此,我们引入了一个通用模型,它包括通常的事务模型,并允许对关联规则进行许多操作。然后,我们将关联规则解释为统计决策规则。这种解释导致了四项决定性措施,其中之一是通常的信心。然后,我们给出了基于这四种决策度量的策略,以选择或构建具有给定结果的关联规则。最后,我们提出了一个实验研究来说明这些策略。本研究是用R语言进行的,使用我们专门为关联规则挖掘构建的R -包。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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