Discovering cluster-based local outliers

Authors:
Zengyou He;Xiaofei Xu;Shengchun Deng
Affiliations:
Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China;Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China;Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

In this paper, we present a new definition for outlier: cluster-based local outlier, which is meaningful and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The experimental results show that our approach outperformed the existing methods on identifying meaningful and interesting outliers.