The main challenges of clustering techniques are to tune the initial cluster centres and to avoid the solution being trapped in the local optima. In this paper, a new metaheuristic algorithm, Differential Search (DS), is used to solve these problems. The DS explores the search space of the given dataset to find the near-optimal cluster centres. The cluster centre-based encoding scheme is used to evolve the cluster centres. The proposed DS-based clustering technique is tested over four real-life datasets. The performance of DS-based clustering is compared with four recently developed metaheuristic techniques. The computational results are encouraging and demonstrate that the DS-based clustering provides better values in terms of precision, recall and G-Measure.