Affiliations: [a] School of Mathematical Sciences, Xiamen University, Xiamen, China
| [b] School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, China
| [c] Department of Artificial Intelligence, Xiamen University, Xiamen, China
| [d] Department of Automation, Xiamen University, Xiamen, China
Correspondence:
[*]
Corresponding author. Jinjin Li, School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, China. E-mail: jinjinli@mnnu.edu.cn.
Abstract: Knowledge reduction is one of critical problems in data mining and information processing. It can simplify the structure of the lattice during the construction of fuzzy-crisp concept lattice. In terms of fuzzy-crisp concept, we develop an order-class matrix to represent extents and intents of concepts, respectively. In order to improve the computing efficiency, it is necessary to reduce the size of lattices as much as possible. Therefore the judgement theorem of meet-irreducible elements is proposed. To deal with attribute reductions, we develop a discernibility Boolean matrix in formal fuzzy contexts by preserving extents of meet-irreducible elements via order-class matrix. A heuristic attribute-reduction algorithm is proposed. Then we extend the proposed model to consistent formal fuzzy decision contexts. Our methods present a new framework for knowledge reduction in formal fuzzy contexts.
Keywords: Attribute reduction, discernibility matrix, fuzzy-crisp concept, meet-irreducible elements
