On the Nature and Types of Anomalies: A Review of Deviations in Data

TL;DR

This paper provides the first comprehensive, domain-independent typology of data anomalies, categorizing them into types and subtypes based on five fundamental dimensions, aiding anomaly detection and explainable data science.

Contribution

It introduces a theoretically principled, domain-independent typology of data anomalies with 63 subtypes, based on five key data-centric dimensions.

Findings

Defines anomaly concept and manifestations clearly

Classifies anomalies into 3 broad groups, 9 types, 63 subtypes

Facilitates evaluation of anomaly detection algorithms

Abstract

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.