广义乘法区间2型模糊划分c均值聚类

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-05-28 DOI:10.1016/j.ins.2025.122356

Chengmao Wu, Yulong Gao

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引用次数: 0

摘要

本文通过隶属度模糊化技术对广义乘性模糊集进行了改进，引入了广义乘性2型模糊集。我们简化了这些集合的复杂度，得到了广义乘法区间2型模糊集合，并给出了它们的运算。在此基础上，提出了一种新的广义乘性区间2型模糊划分，并提出了一种具有对偶模糊加权指数的广义乘性区间2型模糊c均值聚类模型。此外，我们还引入了广义乘法区间2型模糊集的类型约简方法，从而得到了一种新的两级备选迭代聚类算法。实验结果表明，该算法提高了聚类性能，优于广义乘法模糊划分聚类算法。包括归一化互信息（NMI）和调整兰德指数（ARI）在内的性能指标表明，数值数据的改善幅度为1%至4%，图像数据的改善幅度为1%至11%。与现有的2型模糊聚类算法相比，对数值数据和图像数据分别提高了1% ~ 5%和3% ~ 17%。

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Generalized multiplicative interval type-2 fuzzy partition C-means clustering

This paper enhances generalized multiplicative fuzzy sets through a membership value fuzzification technique and introduces generalized multiplicative type-2 fuzzy sets. We simplify the complexity of these sets to create generalized multiplicative interval type-2 fuzzy sets and outline their operations. Building on this foundation, we propose a novel generalized multiplicative interval type-2 fuzzy partition and present a generalized multiplicative interval type-2 fuzzy C-means clustering model with dual fuzzy weighting exponents. Additionally, we introduce a type-reduction method for generalized multiplicative interval type-2 fuzzy sets, leading to a new two-level alternative iteration algorithm for clustering. Experimental results show that our algorithm improves clustering performance, outperforming the generalized multiplicative fuzzy partition clustering algorithm. Performance metrics, including Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI), indicate improvements of 1 % to 4 % for numerical data and 1 % to 11 % for image data. Comparisons with existing type-2 fuzzy clustering algorithms show improvements of 1 % to 5 % for numerical data and 3 % to 17 % for image data.

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.