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
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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
期刊:
影响因子:
18.6
通讯作者:
Liao Huchang
Liao Huchang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Tang Ming;Liao Huchang

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近年来,犹豫模糊语言术语集(HFLTS)因其在表示定性决策过程中的模糊性和犹豫性方面的优势而被广泛用于描述认知复杂的语言信息。信息度量包括距离度量、相似度量、熵度量、包含度量和关联度量,用来刻画语言元素之间的关系。许多决策理论都是以信息度量为基础的。到目前为止,已有学者提出了距离、相似、熵和相关性等度量方法,但还没有专门针对包含性度量的论文。本文致力于填补这一空白,并提出了HFLTS之间的包含度量。我们讨论了HFLTS的距离、相似、包含和熵度量之间的关系。鉴于聚类算法是信息度量的一种重要应用,但在HFLTS环境下基于信息度量的聚类算法的相关文献很少,本文提出了两种分别基于相关性度量和距离度量的聚类算法。最后,以水资源承载能力为例,验证了所提出的聚类算法的适用性。
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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