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. A previously known algorithm for mining representative rules relies on an incorrect mathematical claim, and can be seen to miss part of its intended output; in previous work, two of the authors of the present paper have offered a complete but, often, somewhat slower alternative. Here, we extend this alternative to the case of closure-based redundancy. The empirical validation shows that, in this way, we can improve on the original time efficiency, without sacrificing completeness.

中文翻译：

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基于闭集的代表性关联规则发现

在实践中，关联规则挖掘器的输出通常是巨大的。这就是为什么提出了几种简洁的无损表示，例如“基本”或“代表性”规则。以前已知的用于挖掘代表性规则的算法依赖于不正确的数学声明，并且可以看出它错过了部分预期输出；在之前的工作中，本论文的两位作者提供了一个完整但通常速度较慢的替代方案。在这里，我们将此替代方案扩展到基于闭包的冗余的情况。经验验证表明，通过这种方式，我们可以在不牺牲完整性的情况下提高原来的时间效率。