直觉模糊集的不相似度量及其在模式识别和聚类分析中的应用

IF 0.3 Q4 MATHEMATICS, APPLIED Journal of Applied Mathematics Statistics and Informatics Pub Date : 2023-05-01 DOI:10.2478/jamsi-2023-0004
V. Rani, S. Kumar
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引用次数: 1

摘要

摘要为了防止信息丢失,本文提出了直觉模糊集(ifs)之间的两种不相似度度量,这两种度量考虑了直觉模糊集的隶属度和非隶属度,并且直觉模糊集是模糊集的进一步扩展。此外,我们还检查了所建议的度量标准的特征,以确认其有效性。然后,我们进行了一系列实验,包括数值实验、模式识别和聚类分析,以评估这些指标的有效性。比较结果表明,我们的不相似度度量更直接,易于理解,优于大多数现有方法。
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Dissimilarity measure between intuitionistic Fuzzy sets and its applications in pattern recognition and clustering analysis
Abstract In this study, in order to prevent information loss, we propose two dissimilarity measures between intuitionistic fuzzy sets (IFSs), which consider membership and non-membership degree and IFSs is farther extension of Fuzzy sets (FSs). Additionally, we have examined the characteristics of the proposed metrics to confirm their validity. We then conducted a series of experiments, including numerical experimentation, pattern recognition, and clustering analysis, to evaluate the efficacy of these metrics. The comparative outcomes illustrate that our dissimilarity metrics are more straightforward, easy to understand, and superior to the majority of the existing methods.
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发文量
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审稿时长
20 weeks
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