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.

翻译：异常是数据集中以某种方式不寻常且不符合总体模式的事件。异常的概念通常定义不明确，被视为模糊且依赖于领域。此外，尽管相关出版物已有约250年历史，但迄今为止尚未发布过关于不同类型异常的全面且具体的概述。因此，本研究通过广泛的文献综述，首次提出具有理论原则且领域无关的数据异常类型学，并完整呈现异常类型与子类型的全景。为具体定义异常概念及其不同表现形式，该类型学采用五个维度：数据类型、关系基数性、异常层级、数据结构与数据分布。这些基础且以数据为中心的维度自然产生3大类别、9种基本类型及63种异常子类型。该类型学有助于评估异常检测算法的功能能力，促进可解释数据科学的发展，并为局部与全局异常等相关主题提供洞见。