A survey of outlier detection methodologies and their applications

Authors:
Zhixian Niu;Shuping Shi;Jingyu Sun;Xiu He
Affiliations:
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi, China;College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi, China;College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi, China;College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

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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 approach and reviewed 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.