Wide applications of interval-valued data in various domains have triggered the call for more powerful analytical tools. In light of this, this paper has presented an adaptive dynamic clustering algorithm for interval-valued data, using squared-Wasserstein distance. Experiments on both synthetic data and real data have unveiled the merits of the proposed algorithm.