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Objectively evaluating condensed representations and interestingness measures for frequent itemset mining

机译：客观地评估频繁项集挖掘的压缩表示和有趣度度量

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摘要

Itemset mining approaches, while having been studied for more than 15 years, have been evaluated only on a handful of data sets. In particular, they have never been evaluated on data sets for which the ground truth was known. Thus, it is currently unknown whether itemset mining techniques actually recover underlying patterns. Since the weakness of the algorithmically attractive support/confidence framework became apparent early on, a number of interestingness measures have been proposed. Their utility, however, has not been evaluated, except for attempts to establish congruence with expert opinions. Using an extension of the Quest generator proposed in the original itemset mining paper, we propose to evaluate these measures objectively for the first time, showing how many non-relevant patterns slip through the cracks.
机译：项目集挖掘方法已经研究了15年以上，但仅在少数数据集上进行了评估。特别是，它们从未在已知基础事实的数据集上进行过评估。因此，目前尚不清楚项集挖掘技术是否实际上恢复了底层模式。由于算法吸引人的支持/信心框架的弱点在早期就很明显，因此提出了许多有趣的措施。但是，除了试图与专家意见建立一致性之外，尚未评估其效用。我们使用原始项目集挖掘论文中提出的Quest生成器的扩展，建议第一次客观地评估这些措施，以显示有多少无关的模式穿过裂缝。

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