کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1140944 | 956750 | 2010 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Semi-supervised outlier detection based on fuzzy rough C-means clustering Semi-supervised outlier detection based on fuzzy rough C-means clustering](/preview/png/1140944.png)
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.
Journal: Mathematics and Computers in Simulation - Volume 80, Issue 9, May 2010, Pages 1911–1921