关键词: 离群点检测, 最近邻, 属性约简

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.

Key words: outlier detection, nearest neighborhood, attribute reduction