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:异常现象出现在一个以某种方式不同寻常和不符合一般模式的数据集中,异常现象的概念通常定义不明确,被认为模糊不清,而且取决于领域;此外,尽管就这一专题发表了约250年的出版物,但迄今没有发表过关于不同类型异常现象的全面和具体概览,因此,通过广泛文献审查,本研究报告提供了数据异常现象的第一个理论原则性和根据领域独立的分类,并全面概述了异常现象的类型和子类型;为了具体界定异常现象的概念及其不同表现形式,分类有五个方面:数据类型、关系的主要程度、异常程度、数据结构和数据分布。这些以数据为中心的基本和以数据为中心的方面自然产生了3大类、9个基本类型和63个次类型的异常现象。通过广泛文献审查,这种分类有助于评估异常检测算法的功能能力,有助于解释数据科学,并对地方与全球异常现象等相关专题进行深入了解。