Rough -set -driven approach for attribute reduction in fuzzy formal concept analysis

被引：31
|
作者：
Jose Benitez-Caballero, M. [1 ]
Medina, Jesus [1 ]
Ramirez-Poussa, Eloisa [1 ]
Slezak, Dominik [2 ]
机构：
[1] Univ Cadiz, Dept Math, Cadiz, Spain
[2] Univ Warsaw, Inst Informat, Warsaw, Poland
来源：
FUZZY SETS AND SYSTEMS | 2020年 / 391卷 / 117-138期
关键词：
Fuzzy sets; Attribute reduction; Reduct; Formal concept analysis; Rough set theory; CONCEPT LATTICES; CONCEPT SIMILARITY; DEPENDENCIES;
D O I：
10.1016/j.fss.2019.11.009
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The reduction of the set of attributes is an important preliminary challenge in order to obtain information from knowledge systems. Two remarkable formal tools for extracting such information are Rough Set Theory (RST) and Formal Concept Analysis (FCA), as well as their fuzzy generalizations. This work introduces a new method to reduce attributes in Fuzzy FCA considering the reduction philosophy given in RST and studies its main properties. This method allows us to carry out a deeper study of the relation between these two theories. Moreover, the proposed methodology has been compared with other existing reduction mechanisms. (c) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：117 / 138
页数：22
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