With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the existing clustering algorithms are susceptible to outliers and noises as well as the initial solution. The fuzzy c-ordered-means (FCOM) method can handle outlier and noise problems by using Huber’s M-estimators and Yager’s OWA operator to enhance its robustness. However, the result of the FCOM algorithm is still unstable because its initial centroids are randomly generated. Besides, the attributes’ weight also affect the clustering performance. Thus, this study first proposed an intuitionistic fuzzy weighted c-ordered-means (IFWCOM) algorithm that combines intuitionistic fuzzy sets (IFSs), the feature-weighted and FCOM together to improve the clustering result. Moreover, this study proposed a real-coded genetic algorithm-based IFWCOM (GA-IFWCOM) that employs the genetic algorithm to exploit the global optimal solution of the IFWCOM algorithm. Twelve benchmark datasets were used for verification in the experiment. According to the experimental results, the GA-IFWCOM algorithm achieved better clustering accuracy than the other clustering algorithms for most of the datasets.

随着信息技术的发展，许多领域已经开始使用数据聚类来揭示数据结构并获得有用的信息。现有的大多数聚类算法都容易受到异常值和噪声以及初始解决方案的影响。通过使用Huber的M估计量和Yager的OWA运算符来增强其鲁棒性，模糊c阶均值（FCOM）方法可以处理离群值和噪声问题。但是，FCOM算法的结果仍然不稳定，因为它的初始质心是随机生成的。此外，属性的权重还影响聚类性能。因此，本研究首先提出了一种直觉模糊加权c结合直觉模糊集（IFS），特征加权和FCOM的有序均值（IFWCOM）算法，以改善聚类结果。此外，本研究提出了一种基于实编码遗传算法的IFWCOM（GA-IFWCOM），该算法利用遗传算法来探索IFWCOM算法的全局最优解。在实验中使用了十二个基准数据集进行验证。根据实验结果，对于大多数数据集，GA-IFWCOM算法获得了比其他聚类算法更好的聚类精度。