Abstract
Outlier detection is a data analysis method and has been used to detect and remove anomalous observations from data. In this paper, we firstly introduced some current mainstream outlier detection methodologies, i.e. statistical-based, distance-based, and density-based. Especially, we analyzed distance-based approachandreviewed several kinds of peculiarity factors in detail. Then, we introduced sampled peculiarity factor (SPF) and a SPF-based outlier detection algorithm in order to explore a lower-computational complexity approach to compute peculiarity factor for real world needs in our future work.
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Niu, Z., Shi, S., Sun, J., He, X. (2011). A Survey of Outlier Detection Methodologies and Their Applications. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_50
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DOI: https://doi.org/10.1007/978-3-642-23881-9_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23880-2
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