期刊
INFORMATION FUSION
卷 50, 期 -, 页码 30-42出版社
ELSEVIER
DOI: 10.1016/j.inffus.2018.10.002
关键词
Hesitant fuzzy linguistic term set; Information measure; Inclusion measure; Clustering algorithm; Water resource bearing capacity
资金
- National Natural Science Foundation of China [71771156, 71501135]
- Scientific Research Foundation for Excellent Young Scholars at Sichuan University [2016SCU04A23]
- 2018 Key Research Institute of Humanities and Social Sciences in Sichuan Province [LYC18-02, DSWL18-2, Xq18A01]
- Spark Project of Innovation at Sichuan University [2018hhs-43]
- Graduate Student's Research and Innovation Fund of Sichuan University [2018YJSY038]
Recently, the Hesitant Fuzzy Linguistic Term Sets (HFLTSs) have been widely used to address cognitive complex linguistic information because of its advantage in representing vagueness and hesitation in qualitative decision-making process. Information measures, including distance measure, similarity measure, entropy measure, inclusion measure and correlation measure, are used to characterize the relationships between linguistic elements. Many decision-making theories are based on information measures. Up to now, distance, similarity, entropy and correlation measures have been proposed by scholars but there is no paper focuses on inclusion measure. This paper dedicates to filling this gap and the inclusion measure between HFLTSs are proposed. We discuss the relationships among distance, similarity, inclusion and entropy measures of HFLTSs. Given that clustering algorithm is an important application of information measures but there are few papers related to clustering algorithm based on information measures in the environment of HFLTS, in this paper, we propose two clustering algorithms based on correlation measure and distance measure, respectively. After that, a case study concerning water resource bearing capacity is illustrated to verify the applicability of the proposed clustering algorithms.
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