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A Comprehensive Survey of Anomaly Detection Algorithms

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Abstract

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.

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Notes

  1. Anomaly and outlier are widely used terms. In this work, we will use both terms interchangeably.

  2. Anomaly detection and outlier detection are widely used terms. In this paper, we used both terms interchangeably.

  3. The time complexity of this kind of algorithms can be reduced to \(O(n\log (n))\) by using good indexing structure, but they are not feasible in high dimensional space. Thus we mention time complexities without such index throughout the paper.

  4. Clustered anomalies are anomalies, which form cluster of few points outside of the normal cluster.

  5. Some algorithms choose subspace based on statistical test (e.g. HiCS, CMI) and some choose randomly(e.g. Zero++).

  6. Anomaly detection algorithms based on subspace are required to search for the subspace, which requires additional time, which depends on a search method. We only provide scoring time in a subspace.

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The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the manuscript.

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DS conducted the systematic literature review and examined various outlier detection techniques. DS wrote the first draft of the manuscript. DS made significant contributions to design and structure of review. AT review the work and edit the manuscript. All authors read and approved the final manuscript.

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Samariya, D., Thakkar, A. A Comprehensive Survey of Anomaly Detection Algorithms. Ann. Data. Sci. 10, 829–850 (2023). https://doi.org/10.1007/s40745-021-00362-9

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