A knowledge reduction approach for linguistic concept formal context

2020 ◽  
Vol 524 ◽  
pp. 165-183 ◽  
Author(s):  
Li Zou ◽  
Kuo Pang ◽  
Xiaoying Song ◽  
Ning Kang ◽  
Xin Liu
2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Qing Wan ◽  
Ling Wei

This paper mainly studies attribute reduction which keeps the lattice structure in formal contexts based on the property pictorial diagram. Firstly, the property pictorial diagram of a formal context is defined. Based on such diagram, an attribute reduction approach of concept lattice is achieved. Then, through the relation between an original formal context and its complementary context, an attribute reduct of complementary context concept lattice is obtained, which is also based on the property pictorial diagram of the original formal context. Finally, attribute reducts in property oriented concept lattice and object oriented concept lattice can be acquired by the relations of attribute reduction between these two lattices and concept lattice of complementary context. In addition, a detailed illustrative example is presented.


2014 ◽  
Vol 1014 ◽  
pp. 480-483
Author(s):  
Zhi Hao Peng ◽  
Wei Luo ◽  
An Sheng Deng

Knowledge reduction is one of the basic contents in rough set theory and the most challenging problem in knowledge acquisition. In this paper, an algorithm is proposed, which aims to get all the reducts based on the attributes of the formal context. Experiments show that the algorithm is sound and accurate. Finally, further work and future perspectives are discussed.


2011 ◽  
Vol 219-220 ◽  
pp. 604-607 ◽  
Author(s):  
Xu Yang Wang

Formal concept analysis and rough set theory provide two different methods for data analysis and knowledge processing. Knowledge reduct in this paper combines the two models. For an initial data sets described by formal context, look for absolute necessary attribute sets by applying rough set theory. The sets can image the concepts and hiberarchy structure completely. Then calculate the value cores of attributes values for all objects and delete redundant attributes. At last, delete repeated instances and get the minimum formal context. Construct the concept lattice of the minimum formal context can diminish the size of concept lattice of the initial table at a certain extent.


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