Outlier detection using centrality and center-proximity
- Authors
- Bae, Duck-Ho; Jeong, Seo; Kim, Sang-Wook; Lee, Minsoo
- Issue Date
- Nov-2012
- Publisher
- Association for Computing Machinary, Inc.
- Keywords
- center-proximity; centrality; graph-based outlier detection
- Citation
- ACM International Conference Proceeding Series, pp.2251 - 2254
- Indexed
- SCOPUS
- Journal Title
- ACM International Conference Proceeding Series
- Start Page
- 2251
- End Page
- 2254
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164269
- DOI
- 10.1145/2396761.2398613
- ISSN
- 0000-0000
- 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.
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