Abstract
With the recently rising technologies and numerous applications, the necessity of outlier detection is increasing drastically. Currently, a major variant of outlier detection techniques is witnessed. These techniques played a crucial role in the advancement of fields like medical health, MasterCard fraud, and intrusion detection. However, it is a significant work to spot abnormal behaviours or patterns out from sophisticated data. This paper provides a summary of the outlier detection strategies for the high-dimensional dataset and offers a comprehensive understanding of all basic techniques of outlier detection. This paper provides a comprehensive summary of the ongoing work on anomaly detection techniques, particularly with high-dimensional datasets and data with mixed attributes. The detection of outliers from the given dataset with anomalous data is meaningful work in the area of big data as the data is increasing exponentially every year. Specifically, this paper discusses the current advancement in the field of anomaly detection methods and simultaneously discusses the strengths and limitations of each outlier detection method.
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Das, C., Dubey, A., Rasool, A. (2022). Outlier Detection Techniques: A Comparative Study. In: Patgiri, R., Bandyopadhyay, S., Borah, M.D., Emilia Balas, V. (eds) Edge Analytics. Lecture Notes in Electrical Engineering, vol 869. Springer, Singapore. https://doi.org/10.1007/978-981-19-0019-8_42
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DOI: https://doi.org/10.1007/978-981-19-0019-8_42
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