Attribute reduction of interval-valued fuzzy formal contexts based on rough approximations

LI Tongjun; MENG Qifeng; WU Weizhi

doi:10.16152/j.cnki.xdxbzr.2025-02-010

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Attribute reduction of interval-valued fuzzy formal contexts based on rough approximations

更新时间：2025-04-21

- Attribute reduction of interval-valued fuzzy formal contexts based on rough approximations
- Journal of Northwest University (Natural Science Edition) Vol. 55, Issue 2, Pages: 333-342(2025)
- 作者机构：
  
  浙江海洋大学　信息工程学院，浙江　舟山　 316022
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2025-02-010
  CLC： TP18
- Received：28 September 2024，
  
  Published：25 April 2025
- 稿件说明：
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LI Tongjun, MENG Qifeng, WU Weizhi. Attribute reduction of interval-valued fuzzy formal contexts based on rough approximations[J]. Journal of northwest university (natural science edition), 2025, 55(2): 333-342.
DOI：

LI Tongjun, MENG Qifeng, WU Weizhi. Attribute reduction of interval-valued fuzzy formal contexts based on rough approximations[J]. Journal of northwest university (natural science edition), 2025, 55(2): 333-342. DOI： 10.16152/j.cnki.xdxbzr.2025-02-010.

摘要

将粗造集技术融入形式概念分析是数据分析和信息处理的一种重要方法，其对数据知识发现具有重要意义。在区间值模糊形式背景中定义两种粗糙近似算子，从而导出一种新型单边概念格，即经典-区间值模糊概念格，主要研究新型概念格的属性约简。给出区间值模糊形式背景属性约简和差别矩阵的定义，研究协调属性集判定和约简计算方法。给出协调属性集判定定理和基于差别矩阵的约简计算方法。新概念模型可为区间值模糊形式背景中的知识发现提供新方法，给出的约简方法有利于开发高效的属性约简算法。

Abstract

The integration of rough sets into formal concept analysis is an important method for data analysis and information processing

which is of great significance for data knowledge discovery. Two rough approximation operators are defined in an interval-valued fuzzy formal context

and a new type of one-side concept lattice

namely

crisp-interval-valued fuzzy concept lattice

is derived

and its attribute reduction is meanly studied. According to the definition of attribute reduction of interval-valued fuzzy formal context

the judgement of consistent attribute sets is considered

and by using the techniques of discernibility matrix of rough sets

the calculation method of reducts is explored. The new concept model can provide new approaches for the knowledge discovery of interval-valued fuzzy formal contexts

and the obtained reduction method is beneficial to develop efficient attribute reduction algorithms.

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references

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