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Outlier detection using centrality and center-proximity
- Title
- Outlier detection using centrality and center-proximity
- Authors
- Bae D.-H.; Jeong S.; Kim S.-W.; Lee M.
- Ewha Authors
- 이민수
- SCOPUS Author ID
- 이민수
- Issue Date
- 2012
- Journal Title
- ACM International Conference Proceeding Series
- Citation
- ACM International Conference Proceeding Series, pp. 2251 - 2254
- Indexed
- SCOPUS
- Document Type
- Conference Paper
- 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. © 2012 ACM.
- DOI
- 10.1145/2396761.2398613
- ISBN
- 9781450311564
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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