Outlier detection based on rough membership function

Authors:
Feng Jiang;Yuefei Sui;Cungen Cao
Affiliations:
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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Abstract

In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers — individuals who behave in an unexpected way or have abnormal properties. Outlier detection is critically important in the information-based society. In this paper, we propose a new definition for outliers in rough set theory which exploits the rough membership function. An algorithm to find such outliers in rough set theory is also given. The effectiveness of our method for outlier detection is demonstrated on two publicly available databases.