Anomalies are cases that are in some way unusual and do not appear to fit the general patterns present in the dataset. Several conceptualizations exist to distinguish between different types of anomalies. However, these are either too specific to be generally applicable or so abstract that they neither provide concrete insight into the nature of anomaly types nor facilitate the functional evaluation of anomaly detection algorithms. With the recent criticism on 'black box' algorithms and analytics it has become clear that this is an undesirable situation. This paper therefore introduces a general typology of anomalies that offers a clear and tangible definition of the different types of anomalies in datasets. The typology also facilitates the evaluation of the functional capabilities of anomaly detection algorithms and as a framework assists in analyzing the conceptual levels of data, patterns and anomalies. Finally, it serves as an analytical tool for studying anomaly types from other typologies.
翻译:异常是某些不寻常的情况,似乎不符合数据集中存在的一般模式。有几个概念存在,以区分不同类型的异常,但是,这些概念要么过于具体,无法普遍适用,要么过于抽象,无法具体了解异常类型的性质,也无助于对异常检测算法的功能评估。最近对“黑盒”算法和分析法的批评表明,这是一种不可取的情况。因此,本文件介绍了一种一般的异常类型,为数据集中不同类型的异常提供了明确和具体的定义。这种类型还有助于评估异常检测算法的功能能力,并作为一个框架,协助分析数据、模式和异常现象的概念水平。最后,它作为分析工具,用于研究其他类型异常现象。