Clustering is a well-known approach that is often used in a variety of domain such as data analytics, information retrieval, social mining, image analysis, etc. The literature has described numerous methods based on different clustering concepts; however, partitional clustering methods are commonly used due to their simplicity and ease of implementation. Conventional algorithms such as k-means suffer from some shortcomings like local optima, convergence rate, and population diversity. Heuristic approaches can address these shortfalls of clustering methods effectively. This work utilizes a recently developed meta-heuristic algorithm, called transient search optimization (TSO) to alleviate the issues of traditional clustering algorithms. The proposed work aims to solve the data clustering problem in more effectively and efficiently manner. A number of benchmark clustering datasets are used for assessing the proposed algorithm efficacy, and the outcomes are contrasted with standard clustering metrics like intra, rank and SD measures. The finding demonstrates that the TSO outperforms other algorithms based on intra and rank parameters.