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。现有方法大多对隶属度进行全局迭代，这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，利用粒球对数据进行模糊迭代，数据隶属度仅考虑其所在的两个粒球，从而提升迭代效率。形成的模糊粒球集在不同数据场景下可采用更多处理方法，增强了模糊聚类计算的实用性。