粗糙集和模糊集中概念格的学习  

Study on Concept Lattices in Rough Set Theory and Fuzzy Set Theory

作　　者：万宇文[1] 黄林颖[1] 

机构地区：[1]江西师范大学计算机信息工程学院,江西南昌330022

出　　处：《科技广场》2013年第11期6-11,共6页Science Mosaic

摘　　要：粗糙集理论能够分析和处理不精确、不协调和不完备信息,模糊集理论能够分析和处理模糊的信息,而概念格是知识表示与知识发现的一种有效工具,它们都被成功地应用到多项研究领域。本文利用粗糙集理论中的两个近似算子构造出新的概念格,比较了几种不同的概念格之间的差别与联系,并将形式背景中二元关系扩展到模糊关系,定义了模糊形式背景,从而定义k度模糊形式概念以及k度模糊形式概念格。Rough set theory can analyze and deal with the imprecise, inconsistent and incomplete information, while fuzzy set theory can deal with fuzzy information. The concept lattice is an effective tool to express and discover knowledge. They are all applied in a lot of fields successfully. Through the study of two approximation operators in the rough sets theory, we obtain a new concept lattice, and compare several kinds of concept lattices. By extending the binary relation of the formal context to a fuzzy relation, we define the fuzzy formal context, and then define the k-degree fuzzy formal concept and the k-degree formal concept lattice.

关 键 词：粗糙集 模糊集 形式概念 概念格 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]