Fuzzy C-Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlapping clusters, it involves issues such as sensitivity to noise and outliers, the fuzzification parameter m, and the initial guess of cluster centers. There are various improvements made to FCM to tackle these limitations. One such extension, FCOM, has proved to be an efficient method for handling noise and outliers. However, it still suffers from the last two ones. In the present paper, inspired by FCOM, a new model is designed to solve these issues. Whereas varying the degree of fuzziness m leads to different clusters, determining the appropriate value optimizes solutions. However, manually tuning this parameter can be time-consuming, especially when dealing with large data sets. To mitigate the dependence on this parameter, the proposed model utilizes the entropy theory to control the uncertainty associated with the input data. Extensive evaluations are conducted on benchmark datasets to analyze the impact of m on cluster formation and clustering results. The competitive results confirm the effectiveness of the proposed model for handling fuzziness degree and its capability to accelerate convergence to optimal solutions. Moreover, the results show that the proposed model discovers vague boundaries precisely.

Fuzzy C -Means 是一种著名的模糊聚类技术。尽管FCM可以通过形成重叠聚类来覆盖不确定性问题，但它涉及到对噪声和异常值的敏感性、模糊化参数m以及聚类中心的初始猜测等问题。为了解决这些限制，FCM 进行了各种改进。FCOM 是此类扩展之一，已被证明是处理噪声和异常值的有效方法。然而，它仍然受到后两个的影响。在本论文中，受 FCOM 的启发，设计了一个新模型来解决这些问题。而改变模糊程度m导致不同的集群，确定适当的值可以优化解决方案。然而，手动调整此参数可能非常耗时，尤其是在处理大型数据集时。为了减轻对此参数的依赖性，所提出的模型利用熵理论来控制与输入数据相关的不确定性。对基准数据集进行了广泛的评估，以分析m对聚类形成和聚类结果的影响。竞争结果证实了所提出的模型处理模糊度的有效性及其加速收敛到最优解的能力。此外，结果表明所提出的模型能够准确地发现模糊边界。