Clustering is one of the main data mining tasks with many proven techniques and successful real-world applications. However, in changing environments, the existing systems need to be regularly updated in order to describe in the best possible way an observed phenomenon at each point in time. Since changes lead to uncertainty, the respective systems also require an adequate modeling of the involved kinds of uncertainty. This paper presents a novel method for dynamic clustering called dynamic rough-fuzzy support vector clustering (D-RFSVC). Its main idea is to take advantage of the knowledge acquired in previous cycles to speed up model updating while tracking the structural changes that clusters can experience over time. The core method of the proposed approach is the well-known support vector clustering algorithm, which can be used for large datasets employing powerful optimization techniques. The computational experiments, together with a conceptual and numerical comparative study, highlight the potential D-RFSVC has in dynamic environments.