Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 27, Issue 3, Pages 559-573Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2018.2864661
Keywords
Clustering method; detect intra-relations and group leaders; intuitionistic fuzzy sets; large-scale decision making; sparse representation
Funding
- National Natural Science Foundation of China [71772136, 71722004]
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In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: the sparse representation-based intuitionistic fuzzy clustering-exactly precision algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity of their intuitionistic fuzzy assessment information. The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and the unsupervised clustering method and presents more robust and efficient for LSDM problems.
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