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Outlier detection using centrality and center-proximity
- Outlier detection using centrality and center-proximity
- Bae D.-H.; Jeong S.; Kim S.-W.; Lee M.
- Ewha Authors
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- ACM International Conference Proceeding Series
- 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.
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