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Article type: Research Article
Authors: Xin, Xian-Weia | Song, Ji-Huaa; * | Xue, Zhan-Aob | Peng, Wei-Minga
Affiliations: [a] College of Artificial Intelligence, Beijing Normal University, Beijing, China | [b] College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
Correspondence: [*] Corresponding author. Ji-hua Song, College of artificial intelligence, Beijing Normal University, 100875, Beijing, China. E-mail: [email protected].
Abstract: As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research.
Keywords: Intuitionistic fuzzy, attribute correlation degree, IF3WCL, secondary decision, three-way decision
DOI: 10.3233/JIFS-200002
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1567-1583, 2021
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