基于主成分分析的模糊概念格  

Fuzzy Concept Lattice Based on Principal Component Analysis

作　　者：张露迪 

机构地区：[1]华北水利水电大学数学与统计学院,河南 郑州

出　　处：《应用数学进展》2023年第12期4938-4945,共8页Advances in Applied Mathematics

摘　　要：形式背景中数据的增多,概念的数量就会随之增加。根据数据本身的特点,本文借助统计学方法提出基于主成分分析的模糊概念格以处理数据的多样性和复杂性。通过一般模糊形式背景的矩阵转化,分别定义了主成分属性和主成分模糊形式背景,并且给出了主成分属性算法,得到主成分属性模糊形式背景。利用该方法可以获得对原始形式背景的概念格属性约简,并且通过实例分析说明了该方法的可行性。The increase of data in formal context, the number of concepts will increase accordingly. Based on the characteristics of the data itself, this article proposes a fuzzy concept lattice based on principal component analysis using statistical methods to handle the diversity and complexity of data. Pass through matrix transformation through general fuzzy form background, defined principal compo-nent attributes and principal component fuzzy formal backgrounds respectively, and the principal component attribute algorithm is provided, obtain the fuzzy formal background of principal com-ponent attributes. By using this method, the concept lattice attribute reduction of the original for-mal background can be obtained, and the feasibility of this method was demonstrated through ex-ample analysis.

关 键 词：形式背景 主成分属性 主成分模糊形式背景 属性约简 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]