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Semi-supervised outlier detection based on fuzzy rough C-means clustering

Author

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  • Xue, Zhenxia
  • Shang, Youlin
  • Feng, Aifen

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.

Suggested Citation

  • Xue, Zhenxia & Shang, Youlin & Feng, Aifen, 2010. "Semi-supervised outlier detection based on fuzzy rough C-means clustering," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(9), pages 1911-1921.
    • Handle: RePEc:eee:matcom:v:80:y:2010:i:9:p:1911-1921
    DOI: 10.1016/j.matcom.2010.02.007
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    Cited by:

    1. Abbas Mardani & Mehrbakhsh Nilashi & Jurgita Antucheviciene & Madjid Tavana & Romualdas Bausys & Othman Ibrahim, 2017. "Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature," Complexity, Hindawi, vol. 2017, pages 1-33, October.
    2. Okhli, Kheirolah & Jabbari Nooghabi, Mehdi, 2023. "On the three-component mixture of exponential distributions: A Bayesian framework to model data with multiple lower and upper outliers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 480-500.

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