Managing information measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms
Managing information measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms
复制标题
管理犹豫模糊语言术语集的信息度量及其在设计聚类算法中的应用
DOI:
10.1016/j.inffus.2018.10.002
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发表时间:
2019-10
影响因子:
18.6
通讯作者:
Liao Huchang
中科院分区:
文献类型:
--
作者:
Tang Ming;Liao Huchang
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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