 Semi-supervised outlier detection based on fuzzy rough C-means clusteringZhenxia Xue, Youlin Shang and Aifen Feng Mathematics and Computers in Simulation (MATCOM), 2010, vol. 80, issue 9, 1911-1921 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. Keywords: Pattern recognition; Outlier detection; Semi-supervised learning; Rough sets; Fuzzy sets; C-means clustering (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:80:y:2010:i:9:p:1911-1921 DOI: 10.1016/j.matcom.2010.02.007 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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