基于闭集的关联规则基发现
Closed-set-based Discovery of Bases of Association Rules
摘要：
在实践中，关联规则挖掘器的输出通常是巨大的。这就是为什么已经提出了几种简洁的无损表示，如“基本”或“代表性”规则。我们重新审视Kryszkiewicz（Int. Symp. Intelligent Data Analysis 2001，Springer-Verlag LNCS 2189，350-359）用于挖掘代表性规则。我们表明，它的输出有时是不完整的，由于在其数学验证的疏忽。我们提出了替代完整的发电机，我们扩展到现有的封闭意识的基础上类似，往往小于，代表性的规则，即基础B* 的方法。
英文摘要：
The output of an association rule miner is often huge in practice. This is why several concise lossless representations have been proposed, such as the "essential" or "representative" rules. We revisit the algorithm given by Kryszkiewicz (Int. Symp. Intelligent Data Analysis 2001, Springer-Verlag LNCS 2189, 350-359) for mining representative rules. We show that its output is sometimes incomplete, due to an oversight in its mathematical validation. We propose alternative complete generators and we extend the approach to an existing closure-aware basis similar to, and often smaller than, the representative rules, namely the basis B*.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
