Outlier detection using centrality and center-proximity

Authors:
Duck-Ho Bae;Seo Jeong;Sang-Wook Kim;Minsoo Lee
Affiliations:
Hanyang University, Seoul, South Korea;Hanyang University, Seoul, South Korea;Hanyang University, Seoul, South Korea;Ewha Womans University, Seoul, South Korea
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

An outlier is an object that is considerably dissimilar with the remainder of the dataset. In this paper, we first propose the notion of centrality and center-proximity as novel outlierness measures which can be considered to represent the characteristics of all of the objects in the dataset. We then propose a graph-based outlier detection method which can solve the problems of local density, micro-cluster, and fringe objects. Finally, through extensive experiments, we show the effectiveness of the proposed method.