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Mining dynamic association rules with comments

机译：使用注释挖掘动态关联规则

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摘要

In this paper, we study a new problem of mining dynamic association rules with comments (DAR-C for short). A DAR-C contains not only rule itself, but also its comments that specify when to apply the rule. In order to formalize this problem, we first present the expression method of candidate effective time slots, and then propose several definitions concerning DAR-C. Subsequently, two algorithms, namely ITS2 and EFP-Growth2, are developed for handling the problem of mining DAR-C. In particular, ITS2 is an improved two-stage dynamic association rule mining algorithm, while EFP-Growth2 is based on the EFP-tree structure and is suitable for mining high-density mass data. Extensive experimental results demonstrate that the efficiency and scalability of our proposed two algorithms (i.e., ITS2 and EFP-Growth2) on DAR-C mining tasks, and their practicability on real retail dataset.
机译：在本文中，我们研究了一个带有注释的动态关联规则挖掘新问题（简称DAR-C）。 DAR-C不仅包含规则本身，还包含指定何时应用规则的注释。为了使这个问题形式化，我们首先提出候选有效时隙的表达方法，然后提出关于DAR-C的几种定义。随后，开发了两种算法ITS2和EFP-Growth2来处理DAR-C挖掘问题。特别是，ITS2是一种改进的两阶段动态关联规则挖掘算法，而EFP-Growth2基于EFP-tree结构，适用于挖掘高密度海量数据。大量的实验结果表明，我们提出的两种算法（即ITS2和EFP-Growth2）在DAR-C挖掘任务上的效率和可扩展性及其在实际零售数据集上的实用性。

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