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Intuitionistic fuzzy linguistic clustering algorithm based on a new correlation coefficient for intuitionistic fuzzy linguistic information

机译:直观模糊语言聚类算法基于新相关系数的直观模糊语言信息

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摘要

For observations to be classified, when scoring rules are imprecise or the cost of their computation is too high, the clustering method under linguistic information is necessary. Considering the accuracy of intuitionistic fuzzy linguistic variable in expressing experts' opinions, a clustering algorithm is presented in this paper. Firstly, the concept of triangular intuitionistic fuzzy linguistic variables (TIFLVs) is introduced, and a new formula is developed for calculating correlation coefficient of TIFLVs. Then, the correlation coefficient plays a central role in our modified -cutting algorithm for clustering, which is utilized to construct an equivalence correlation matrix. In addition, a silhouette cluster validity index of TIFLVs is proposed to revise the results of clustering. Finally, the experimental results demonstrate the application and practicability of the clustering algorithm.
机译:对于分类的观察,当评分规则不精确或计算的成本太高时,需要在语言信息下进行聚类方法。考虑到直觉模糊语言变量在表达专家意见中的准确性,本文提出了一种聚类算法。首先,介绍了三角形直觉模糊语言变量(TiFLV)的概念,并且开发了用于计算TiFLV的相关系数的新公式。然后,相关系数在我们修改的-Cutting算法中发挥着核心作用,用于聚类,其用于构造等效相关矩阵。此外,提出了TIFLV的剪影集群有效性索引来修改聚类结果。最后,实验结果证明了聚类算法的应用和实用性。

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