Sequence-based Mixed Attribute Outlier Detection in Neighborhood Rough Sets
YUAN Zhong1,2,ZHANG Xian-yong1,2,FENG Shan1
1（College of Mathematics and Software Science,Sichuan Normal University,Chengdu 610068,China）2（Institute of Intelligent Information and Quantum Information,Sichuan Normal University,Chengdu 610068,China）
Abstract：Outlier detection has extensive applications.However,the outlier detection method based on classical rough sets cannot effectively deal with the numerical attribute data,and thus a new method of mixed attribute outlier detection is proposed based on sequence.The method constructs the attribute sequence by the variance of each attribute value,and the sequence attribute set is defined to construct the neighborhood class sequence.Then,the outlier is detected by analyzing the object change in the neighborhood class sequence,and the corresponding outlier detection algorithm (Sequence-based Mixed Attribute Outlier Detection,SMOAD) is designed,to improve the traditional one-by-one calculation pattern when computing neighborhood covering of a single attribute.Finally,the experiments are compared with main outlier detection methods via the UCI standard data sets,and the results show the effectiveness of the proposed method.
2018, Vol. 39 
