鉴别模式的特征化
Characterizing Discriminative Patterns
摘要：
区分模式是在某些类别中与其他类别相比以不成比例的频率出现的关联模式，并且已经在新兴模式和对比集等名称下进行了研究。这种模式已经证明了相当大的分类和亚组发现的价值，但在一个歧视性的模式项目之间的相互作用的类型缺乏详细的了解。为了解决这个问题，我们建议根据四种类型的项目交互：（i）驾驶员-乘客，（ii）连贯，（iii）独立添加剂和（iv）协同超越独立添加剂的歧视模式进行分类。最后三个中的任何一个都具有实际重要性，后两个表示模式对其子集的区分能力的增益。协同模式是最具限制性的，但也许是最有趣的，因为它们捕捉到了合作效果。对于遗传研究等领域，区分这些类型的模式至关重要，因为每种模式都会产生非常不同的生物学解释。对于一般域，表征提供了一个新的观点的性质的歧视性模式在数据集，产生的见解超出了目前的方法，主要集中在基于模式的分类和子组发现。本文提出了一个全面的讨论，定义这四种模式类型，并调查他们的属性和他们之间的关系。此外，这些想法是探索各种数据集（10 UCI数据集，一个基因表达数据集和两个遗传变异数据集）。结果表明，不同类型的模式的存在，特点和统计意义。它们还说明了模式表征如何为判别模式挖掘和不同数据集的判别结构提供新的见解。
英文摘要：
Discriminative patterns are association patterns that occur with disproportionate frequency in some classes versus others, and have been studied under names such as emerging patterns and contrast sets. Such patterns have demonstrated considerable value for classification and subgroup discovery, but a detailed understanding of the types of interactions among items in a discriminative pattern is lacking. To address this issue, we propose to categorize discriminative patterns according to four types of item interaction: (i) driver-passenger, (ii) coherent, (iii) independent additive and (iv) synergistic beyond independent additive. Either of the last three is of practical importance, with the latter two representing a gain in the discriminative power of a pattern over its subsets. Synergistic patterns are most restrictive, but perhaps the most interesting since they capture a cooperative effect. For domains such as genetic research, differentiating among these types of patterns is critical since each yields very different biological interpretations. For general domains, the characterization provides a novel view of the nature of the discriminative patterns in a dataset, which yields insights beyond those provided by current approaches that focus mostly on pattern-based classification and subgroup discovery. This paper presents a comprehensive discussion that defines these four pattern types and investigates their properties and their relationship to one another. In addition, these ideas are explored for a variety of datasets (ten UCI datasets, one gene expression dataset and two genetic-variation datasets). The results demonstrate the existence, characteristics and statistical significance of the different types of patterns. They also illustrate how pattern characterization can provide novel insights into discriminative pattern mining and the discriminative structure of different datasets.
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