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Vague concept lattice reduction using granular computing and vague entropy

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  • Singh, Prem Kumar

Abstract

Recently, the calculus of Formal Concept Analysis (FCA) in the fuzzy setting is prolonged in bipolar fuzzy space for precise analysis of fuzzy attributes. In this process it is addressed that the attributes like bald (or tadpole) or not bald (not tadpole) cannot be defined through a precise or sharp boundaries. To deal with them, some evidence to support (i.e. true tA membership-values) or reject (i.e. false fA membership-values) the attributes is required in the given boundary 0≤tA+fA≤1. Hence, the proposed method tried to provide the mathematical algebra of vague concept lattice and its navigation at user defined granules with an illustrative example. In addition, the vague entropy measurement is also computed to validate the results.

Suggested Citation

  • Singh, Prem Kumar, 2019. "Vague concept lattice reduction using granular computing and vague entropy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 165(C), pages 56-73.
    • Handle: RePEc:eee:matcom:v:165:y:2019:i:c:p:56-73
    DOI: 10.1016/j.matcom.2019.02.007
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    References listed on IDEAS

    as
    1. Muhammad Akram & Wieslaw A. Dudek & M. Murtaza Yousaf, 2014. "Regularity in Vague Intersection Graphs and Vague Line Graphs," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-10, April.
    2. Singh, Prem Kumar, 2017. "Complex vague set based concept lattice," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 145-153.
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