Outlier detection algorithm based on global nearest neighborhood
HU Yun1,2, SHI Jun1, WANG Chong-jun2, LI Hui1
1.School of Computer Engineering, Huaihai Institute of Technology, Lianyungang Jiangsu 222000, China
2.Department of Computer Science and Technology, Nanjing University, Nanjing Jiangsu 210000, China
Abstract��Traditional outlier detection algorithms fall short in efficiency for their holistic nearest neighboring search mechanism and need to be improved. This paper proposed a new outlier detection method using attribute reduction techniques which enabled the algorithm to focus its detecting scope only on the most meaningful attributes of the data space. Under the reduced set of attributes, a concept of neighborhood-based outlier factor was defined for the algorithm to judge data's abnormity. The combined strategy can reduce the searching complexity significantly and find more reasonable outliers in dataset. The results of experiments also demonstrate promising adaptability and effectiveness of the proposed approach.
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HU Yun SHI Jun WANG Chong-jun LI Hui. Outlier detection algorithm based on global nearest neighborhood. Journal of Computer Applications, 2011, 31(10): 2778-2781.
2011, Vol. 31 
