论异常的性质与类型：数据偏差综述
On the Nature and Types of Anomalies: A Review of Deviations in Data
AI总结：
本文通过文献综述首次提出领域无关的数据异常类型学，基于五个数据维度划分出3组、9类、63种子类型，为异常检测算法评估和可解释数据科学提供基础。
摘要：
异常是数据集中以某种方式表现异常、不符合一般模式的出现。异常的概念通常界定不清，被认为模糊且依赖于领域。此外，尽管关于该主题已有约250年的文献发表，但迄今为止尚未发表过对不同类型异常的全面而具体的概述。因此，通过广泛的文献综述，本研究首次提出了一种理论上具有原则性且领域无关的数据异常类型学，并给出了异常类型和子类型的完整概览。为了具体定义异常的概念及其不同表现形式，该类型学采用了五个维度：数据类型、关系的基数、异常层级、数据结构和数据分布。这些基础且以数据为中心的维度自然产生了3个大的异常组、9种基本类型和63种子类型。该类型学有助于评估异常检测算法的功能能力，有助于可解释数据科学，并为局部异常与全局异常等相关主题提供见解。
英文摘要：
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill-defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of publications on the topic, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations, the typology employs five dimensions: data type, cardinality of relationship, anomaly level, data structure, and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types, and 63 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.
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。
