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Introduction to formal concept analysis and its applications in reliability engineering

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  • Rocco, Claudio M.
  • Hernandez-Perdomo, Elvis
  • Mun, Johnathan

Abstract

Formal Analysis of Concepts (FCA) is a method of data analysis that helps to study the relationship between a set of objects and a set of attributes (the formal context). FCA not only allows detecting data groups (concepts) and their graphical visualization, but also extracting rules that could reveal the underlying structure of the analyzed context. The main idea of this paper is to present the fundamentals of FCA and how it can be used in reliability engineering problems. To this aim, examples in reliability engineering, from both the literature and authors’ experience, have been selected for analysis. Comments on the new insights provided by FCA are also highlighted. Finally, the results from the examples selected show that other reliability areas could benefit from using an FCA-based approach.

Suggested Citation

  • Rocco, Claudio M. & Hernandez-Perdomo, Elvis & Mun, Johnathan, 2020. "Introduction to formal concept analysis and its applications in reliability engineering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305032
    DOI: 10.1016/j.ress.2020.107002
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    References listed on IDEAS

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    1. Peter Butka & Jozef Pócs & Jana Pócsová, 2013. "Representation of Fuzzy Concept Lattices in the Framework of Classical FCA," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-7, November.
    2. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    3. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2017. "AC power flow importance measures considering multi-element failures," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 89-97.
    4. Lenca, Philippe & Meyer, Patrick & Vaillant, Benoit & Lallich, Stephane, 2008. "On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid," European Journal of Operational Research, Elsevier, vol. 184(2), pages 610-626, January.
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    Cited by:

    1. Jianfeng Xu & Chenglei Wu & Jilin Xu & Lan Liu & Yuanjian Zhang, 2023. "Stream Convolution for Attribute Reduction of Concept Lattices," Mathematics, MDPI, vol. 11(17), pages 1-19, August.

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