Rule acquisition and attribute reduction in real decision formal contexts

被引:29
|
作者
Yang, Hong-Zhi [2 ]
Yee, Leung [3 ,4 ]
Shao, Ming-Wen [1 ]
机构
[1] Shihezi Univ, Coll Informat Sci & Technol, Shihezi 832000, Xinjiang, Peoples R China
[2] Xi An Jiao Tong Univ, Fac Sci, Xian 710049, Shaanxi, Peoples R China
[3] Univ Hong Kong, Dept Geog & Resource Management, Ctr Environm Policy & Resource Management, Hong Kong, Hong Kong, Peoples R China
[4] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Attribute reduction; Concept lattice; Formal concept analysis; Real relation; Rules acquisition; CONCEPT LATTICES; KNOWLEDGE REDUCTION; ROUGH; PRECISION; SYSTEMS; MODEL;
D O I
10.1007/s00500-010-0578-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Formal Concept Analysis of real set formal contexts is a generalization of classical formal contexts. By dividing the attributes into condition attributes and decision attributes, the notion of real decision formal contexts is introduced. Based on an implication mapping, problems of rule acquisition and attribute reduction of real decision formal contexts are examined. The extraction of "if-then" rules from the real decision formal contexts, and the approach to attribute reduction of the real decision formal contexts are discussed. By the proposed approach, attributes which are non-essential to the maximal s rules or l rules (to be defined later in the text) can be removed. Furthermore, discernibility matrices and discernibility functions for computing the attribute reducts of the real decision formal contexts are constructed to determine all attribute reducts of the real set formal contexts without affecting the results of the acquired maximal s rules or l rules.
引用
收藏
页码:1115 / 1128
页数:14
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