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Generalized closed itemsets for association rule mining

机译：关联规则挖掘的通用封闭项目集

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The output of Boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning "uninteresting" rules following the observation that most rules can be derived from other rules. We propose a new framework, namely, the generalized closed (or g-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating g-closed itemsets provides significant performance improvements on a variety of databases.
机译：布尔关联规则挖掘算法的输出通常对于手动检查而言太大。对于密集数据集，甚至生成所有频繁项集通常是不切实际的。封闭项集方法通过观察到大多数规则可以从其他规则派生的现象，通过修剪“无趣的”规则来处理此信息过载。我们提出了一个新的框架，即广义封闭（或g封闭）项集框架。通过在项目集支持的准确性中允许较小的容忍度，我们证明了这种冗余规则的数量远远超过了以前的估计。我们的方案可以集成到逐级算法（Apriori）和两次通过算法（ARMOR）中。我们通过测量输出大小的减少以及响应时间来评估其性能。我们的实验表明，合并g封闭项集可以在各种数据库上显着提高性能。

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