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.
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Jiang, F., Sui, Y., Cao, C. (2006). Outlier Detection Based on Rough Membership Function. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_41
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DOI: https://doi.org/10.1007/11908029_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
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