ATTRIBUTE SIGNIFICANCE, CONSISTENCY MEASURE AND ATTRIBUTE REDUCTION IN FORMAL CONCEPT ANALYSIS

被引：22

作者：

Huang, C. ^{[1
]}

Li, J. ^{[1
]}

Dias, S. M. ^{[2
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Sci, Kunming, Yunnan, Peoples R China

[2] Fed Serv Data Proc SERPRO, Belo Horizonte, MG, Brazil

来源：

NEURAL NETWORK WORLD | 2016年 / 26卷 / 06期

基金：

中国国家自然科学基金;

关键词：

formal concept analysis; information entropy; attribute significance; consistency; KNOWLEDGE REDUCTION; CONCEPT LATTICES; ROUGH SET; OBJECT; JBOS;

D O I：

10.14311/NNW.2016.26.035

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One focus of data analysis in formal concept analysis is attribute significance measure, and another is attribute reduction. From the perspective of information granules, we propose information entropy in formal contexts and conditional information entropy in formal decision contexts, and they are further used to measure attribute significance. Moreover, an approach is presented to measure the consistency of a formal decision context in preparation for calculating reducts. Finally, heuristic ideas are integrated with reduction technique to achieve the task of calculating reducts of an inconsistent data set.

引用

页码：607 / 623

页数：17

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