基于分类法的大数据集翻转相关性挖掘
Mining Flipping Correlations from Large Datasets with Taxonomies
摘要：
本文介绍了一种新的模式--翻转相关模式。翻转模式是通过对比不同抽象层次的项目之间的相关性来获得的。它们代表了令人惊讶的正相关和负相关，这些相关对于给定的抽象级别是特定的，并且当项目被概括到更高的抽象级别时，这些相关从正到负“翻转”，反之亦然。我们设计了一个有效的算法，寻找翻转相关性，Flipper算法，它优于天真的模式挖掘方法的几个数量级。我们将Flipper应用于现实生活中的数据集，并表明发现的模式是非冗余的，令人惊讶的和可操作的。Flipper在中低支持度的项集中发现了强烈的对比相关性，而现有技术无法处理在此频率范围内的模式发现。
英文摘要：
In this paper we introduce a new type of pattern -- a flipping correlation pattern. The flipping patterns are obtained from contrasting the correlations between items at different levels of abstraction. They represent surprising correlations, both positive and negative, which are specific for a given abstraction level, and which "flip" from positive to negative and vice versa when items are generalized to a higher level of abstraction. We design an efficient algorithm for finding flipping correlations, the Flipper algorithm, which outperforms naive pattern mining methods by several orders of magnitude. We apply Flipper to real-life datasets and show that the discovered patterns are non-redundant, surprising and actionable. Flipper finds strong contrasting correlations in itemsets with low-to-medium support, while existing techniques cannot handle the pattern discovery in this frequency range.
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。
