一种量化名义异常度的新颖框架
A novel framework for quantifying nominal outlyingness
AI总结：
本文提出了一种基于关联规则挖掘的名义异常度量化框架，通过计算异常分数并引入变量贡献和异常度深度提升可解释性，在合成与公开数据集上表现优于或媲美现有频繁模式挖掘算法。
摘要：
离群点检测是一种重要的数据挖掘工具，在处理名义数据时变得尤为具有挑战性。首先，将观测值标记为异常需要一个明确定义的名义异常度概念。本文提出了一个名义异常度的定义，并引入了一个量化名义数据异常度的通用框架。所提出的框架利用了关联规则挖掘文献中的思想，可用于计算指示名义观测值异常程度的分数。本文提出了确定相关超参数值的方法，并引入了变量贡献和异常度深度的概念，以增强结果的可解释性。所提出的框架在合成和公开数据集上进行了评估，表现出与最先进的频繁模式挖掘算法相当的性能，甚至在某些情况下优于它们。所提出的想法可以作为一种工具，用于评估观测值与其他数据差异的程度，前提是假设名义水平序列是由具有不同事件概率的Multinomial分布生成的。
英文摘要：
Outlier detection is an important data mining tool that becomes particularly challenging when dealing with nominal data. First and foremost, flagging observations as outlying requires a well-defined notion of nominal outlyingness. This paper presents a definition of nominal outlyingness and introduces a general framework for quantifying outlyingness of nominal data. The proposed framework makes use of ideas from the association rule mining literature and can be used for calculating scores that indicate how outlying a nominal observation is. Methods for determining the involved hyperparameter values are presented and the concepts of variable contributions and outlyingness depth are introduced, in an attempt to enhance interpretability of the results. The proposed framework is evaluated on both synthetic and publicly available data sets, demonstrating comparable performance to state-of-the-art frequent pattern mining algorithms and even outperforming them in certain cases. The ideas presented can serve as a tool for assessing the degree to which an observation differs from the rest of the data, under the assumption of sequences of nominal levels having been generated from a Multinomial distribution with varying event probabilities.
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。
