Knowledge Granulation in Interval-Valued Information Systems Based on Maximal Consistent Blocks

被引：0
|
作者：
Zhang, Nan [1 ]
Yue, Xiaodong [2 ]
机构：
[1] Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Shandong, Peoples R China
[2] Shanghai Univ, Sch Engn & Comp Sci, Shanghai PT-200444, Peoples R China
来源：
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014 | 2014年 / 8818卷
基金：
中国国家自然科学基金;
关键词：
rough set theory; knowledge granulation; uncertainty measure; ROUGH ENTROPY; UNCERTAINTY;
D O I：
10.1007/978-3-319-11740-9_5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rough set theory, proposed by Pawlak in the early 1980s, is an extension of the classical set theory for modeling uncertainty or imprecision information. In this paper, we investigate partial relations and propose the concept of knowledge granulation based on the maximal consistent block in interval-valued information systems. The knowledge granulation can provide important approaches to measuring the discernibility of different knowledge in interval-valued information systems. These results in this paper may be helpful for understanding the essence of rough approximation and attribute reduction in interval-valued information systems.
引用
收藏
页码：49 / 58
页数：10
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