A Comprehensive Survey of Anomaly Detection Algorithms

• Published in: Annals of data science Vol. 10; no. 3; pp. 829 - 850
• Main Authors: Samariya, Durgesh, Thakkar, Amit
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Artificial Intelligence; Business and Management; Clustering; Data analysis; Data mining; Data points; Data science; Datasets; Deep learning; Economics; Finance; Insurance; Management; Outliers (statistics); Statistics; Statistics for Business; Outlier detection; Survey; Anomaly; Anomaly detection; Outlier analysis
• ISSN: 2198-5804; 2198-5812
• DOI: 10.1007/s40745-021-00362-9

Anomaly or outlier detection is consider as one of the vital application of data mining, which deals with anomalies or outliers. Anomalies are considered as data points that are dramatically different from the rest of the data points. In this survey, we comprehensively present anomaly detection algorithms in an organized manner. We begin this survey with the definition of anomaly, then provide essential elements of anomaly detection, such as different types of anomaly, different application domains, and evaluation measures. Such anomaly detection algorithms are categorized in seven categories based on their working mechanisms, which includes total of 52 algorithms. The categories are anomaly detection algorithms based on statistics, density, distance, clustering, isolation, ensemble and subspace. For each category, we provide the time complexity of each algorithm and their general advantages and disadvantages. In the end, we compared all discussed anomaly detection algorithms in detail.