TY - GEN
T1 - Outlier detection using centrality and center-proximity
AU - Bae, Duck Ho
AU - Jeong, Seo
AU - Kim, Sang Wook
AU - Lee, Minsoo
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - center-proximity
KW - centrality
KW - graph-based outlier detection
UR - http://www.scopus.com/inward/record.url?scp=84871089735&partnerID=8YFLogxK
U2 - 10.1145/2396761.2398613
DO - 10.1145/2396761.2398613
M3 - Conference contribution
AN - SCOPUS:84871089735
SN - 9781450311564
T3 - ACM International Conference Proceeding Series
SP - 2251
EP - 2254
BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Y2 - 29 October 2012 through 2 November 2012
ER -