In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

翻译：近年来，模糊聚类问题受到广泛关注。现有方法的隶属度迭代大多从全局角度出发，在噪声环境下存在显著问题，且对样本量差异较大的聚类进行迭代计算时，准确性和效率均不足。本文从大规模优先策略出发，利用颗粒球对数据进行模糊迭代，数据的隶属度仅考虑其所在的两个颗粒球，从而提高了迭代效率。形成的模糊颗粒球集可针对不同数据场景采用更多处理方法，增强了模糊聚类计算的实用性。