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<p>With the rapid development of information technology, the volume of data has grown explosively. Efficiently extracting valuable information from large amounts of data has become a critical challenge. In the data pre-processing phase, attribute reduction helps maintain data validity, improves processing efficiency, and reduces computational complexity. However, research on visual attribute reduction in fuzzy environments remains relatively scarce. To fill this research gap, this paper proposes a visualization technique based on fuzzy hypergraphs in the rough set theory framework, aiming to simplify the attribute identification and reduction process in fuzzy formal contexts. First, this paper illustrates how hypergraphs can be constructed from a fuzzy formal context (FCC), and the process of inverse construction. When a threshold is given, the uniqueness of the relationship between the hypergraph and the corresponding FCC is determined. Based on rough set theory, an equivalence relation <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\textbf{T}\)</EquationSource> </InlineEquation> is established in the object set, and a method is proposed to identify redundant elements in <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\textbf{T}\)</EquationSource> </InlineEquation> using hypergraphs. The relationship between the equivalence relation <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\textbf{T}\)</EquationSource> </InlineEquation> and attribute reduction is explored, thereby simplifying attribute identification and improving interpretability. Finally, experimental results under different threshold values are compared and analyzed using Matlab R2016a, validating the effectiveness of the proposed algorithm. The importance of threshold values in optimizing attribute reduction and computational efficiency is also emphasized.</p>

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An attribute reduction method of fuzzy formal contexts based on fuzzy hypergraphs in rough sets

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Zengtai Gong,
  • Lele He

摘要

With the rapid development of information technology, the volume of data has grown explosively. Efficiently extracting valuable information from large amounts of data has become a critical challenge. In the data pre-processing phase, attribute reduction helps maintain data validity, improves processing efficiency, and reduces computational complexity. However, research on visual attribute reduction in fuzzy environments remains relatively scarce. To fill this research gap, this paper proposes a visualization technique based on fuzzy hypergraphs in the rough set theory framework, aiming to simplify the attribute identification and reduction process in fuzzy formal contexts. First, this paper illustrates how hypergraphs can be constructed from a fuzzy formal context (FCC), and the process of inverse construction. When a threshold is given, the uniqueness of the relationship between the hypergraph and the corresponding FCC is determined. Based on rough set theory, an equivalence relation \(\textbf{T}\) is established in the object set, and a method is proposed to identify redundant elements in \(\textbf{T}\) using hypergraphs. The relationship between the equivalence relation \(\textbf{T}\) and attribute reduction is explored, thereby simplifying attribute identification and improving interpretability. Finally, experimental results under different threshold values are compared and analyzed using Matlab R2016a, validating the effectiveness of the proposed algorithm. The importance of threshold values in optimizing attribute reduction and computational efficiency is also emphasized.