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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
이민수scopus
Issue Date
2012
Journal Title
ACM International Conference Proceeding Series
Indexed
SCOPUS scopus
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:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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