Semi-supervised outlier detection based on fuzzy rough C-means clustering |
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Authors: | Zhenxia Xue Youlin ShangAifen Feng |
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Affiliation: | School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471003, China |
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Abstract: | This paper presents a fuzzy rough semi-supervised outlier detection (FRSSOD) approach with the help of some labeled samples and fuzzy rough C-means clustering. This method introduces an objective function, which minimizes the sum squared error of clustering results and the deviation from known labeled examples as well as the number of outliers. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary by using fuzzy rough C-means clustering and only those points located in boundary can be further discussed the possibility to be reassigned as outliers. As a result, this method can obtain better clustering results for normal points and better accuracy for outlier detection. Experiment results show that the proposed method, on average, keep, or improve the detection precision and reduce false alarm rate as well as reduce the number of candidate outliers to be discussed. |
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Keywords: | Pattern recognition Outlier detection Semi-supervised learning Rough sets Fuzzy sets C-means clustering |
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