In this study, a novel approach, interval fuzzy c-regression models with competitive agglomeration (IFCRMCA), is proposed to deal with the symbolic interval-valued data. The proposed IFCRMCA approach can identify the partition of the interval-valued data using both the distances to the cluster centers and the errors of interval regression models for each cluster. Due to the concepts of competitive agglomeration is used in the proposed approach, the pre-determination of the cluster number in the proposed IFCRMCA is not necessary. Various real experiments are carried on and the experimentally results shows that the proposed approaches are superior to the existing approaches.

中文翻译：

---

具有符号集值数据的竞争集结的区间模糊c回归模型

在这项研究中，提出了一种新的方法，具有竞争性集聚的区间模糊c回归模型（IFCRMCA），用于处理符号区间值数据。提出的IFCRMCA方法可以使用到聚类中心的距离以及每个聚类的间隔回归模型的误差来识别间隔值数据的分区。由于在提议的方法中使用了竞争聚集的概念，因此在提议的IFCRMCA中不必预先确定簇数。进行了各种实际实验，实验结果表明所提出的方法优于现有方法。