Rule acquisition and attribute reduction in real decision formal contexts
 HongZhi Yang,
 Leung Yee,
 MingWen Shao
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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 nonessential 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.
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 Title
 Rule acquisition and attribute reduction in real decision formal contexts
 Journal

Soft Computing
Volume 15, Issue 6 , pp 11151128
 Cover Date
 20110601
 DOI
 10.1007/s005000100578y
 Print ISSN
 14327643
 Online ISSN
 14337479
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Attribute reduction
 Concept lattice
 Formal concept analysis
 Real relation
 Rules acquisition
 Industry Sectors
 Authors

 HongZhi Yang ^{(1)}
 Leung Yee ^{(2)} ^{(3)}
 MingWen Shao ^{(4)}
 Author Affiliations

 1. Faculty of Science, Xi’an Jiaotong University, Xi’an, 710049, Shaan’xi, People’s Republic of China
 2. Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, University of Hong Kong, Hong Kong, People’s Republic of China
 3. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, People’s Republic of China
 4. College of Information Science and Technology, Shihezi University, Shihezi, 832000, Xinjiang, People’s Republic of China