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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Jiang, F., Sui, Y., & Cao, C. (2006). Outlier detection based on rough membership function. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4259 LNAI, pp. 388–397). Springer Verlag. https://doi.org/10.1007/11908029_41
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