Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.

中文翻译：

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基于FCM和增强对数粒子群算法（ELPSO）的混合模糊聚类方法

模糊c均值（FCM）是最著名的聚类方法之一，可以自动组织各种数据集并获得准确的分类，但它有陷入局部极小值的倾向。为了克服这些弱点，文献中提出了一些混合 PSO 和 FCM 进行聚类的方法，并且证明这些混合方法比传统分区聚类方法具有更高的精度，而基于 PSO 的聚类方法在与分区聚类技术相比，当前的 PSO 算法需要调整一系列参数才能找到好的解决方案。因此，本文提出了一种混合模糊聚类方法，称为FCM-ELPSO，旨在解决这些缺点。它将 FCM 与 PSO 的改进版本（称为 ELPSO）相结合，采用新的增强对数惯性权重策略，以在勘探和开发之间提供更好的平衡。这种新的混合方法使用PBM(F)指标和目标函数值作为聚类有效性指标来评估聚类效果。为了验证算法的有效性，进行了两种类型的实验，包括PSO聚类和混合聚类。实验表明，该方法显着提高了收敛速度和聚类效果。