Abstract
In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.
This work is supported by the National NSF of China (60273019 and 60073017), the National 973 Project of China (G1999032701), Ministry of Science and Technology (2001CCA03000) and the National Laboratory of Software Development Environment.
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Jiang, F., Sui, Y., Cao, C. (2005). Outlier Detection Using Rough Set Theory. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_9
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DOI: https://doi.org/10.1007/11548706_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
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