[1]ZHANG Xiaohe,CHEN Degang,MI Jusheng.Object-weighted concept lattice based on information entropy[J].CAAI Transactions on Intelligent Systems,2020,15(6):1097-1103.[doi:10.11992/tis.202006043]
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Object-weighted concept lattice based on information entropy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 6 Page number: 1097-1103 Column: 学术论文—人工智能基础 Public date: 2020-11-05
Title:
Object-weighted concept lattice based on information entropy
Author(s):
ZHANG Xiaohe1;  CHEN Degang1;  MI Jusheng2
1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102200, China;
2. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China
Keywords:
formal context; context; information entropy; granular computing; concept lattice; decision rules; weight value; data mining
CLC:
TP18;O236
DOI:
10.11992/tis.202006043
Abstract:
In the era of big data, it is becoming increasingly difficult to construct concept lattices due to the increasingly large scale of data. To objectively reflect hidden information, redundant objects and attributes should be deleted and data size should be reduced to construct simple concept lattices, thus, facilitating users to acquire knowledge efficiently. In this study, to prevent subjective factors, the information entropy of an attribute in the formal context is used to obtain a single attribute weight and the attribute weight of the object is, then, calculated using the mean value method and the importance deviation of the object is calculated by standard deviation. By setting the attribute weight, object weight, and object importance deviation threshold, an object-weighted concept lattice is constructed. An example is provided to verify the effectiveness of this method in removing redundant concepts and simplifying the construction of concept lattices.
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