<p>The business world is concentric around the imperative of information extraction and analysis. The information extraction processes are blended with different data mining techniques like clustering, classification, etc. Clustering is an explorative technique that extracts imperative information from large databases. The adaptability of clustering methods in various disciplines leads to momentous research; immersive work is in this field. Several algorithms are reported for clustering to handle dissimilar clustering problems like initialization, local optima, diversity, and convergence rate. In addition to this, clustering methods are automated, improved/hybridized to obtain new/robust clustering algorithms. An extensive literature survey was carried out to provide in-depth knowledge of partitional clustering algorithms, datasets, and performance measures. This study considers the last twelve years substantial work on partitional clustering and well summarized to promote profound understanding and in-depth learning. The outcome of this research would highlight the key aspects determining the most suitable partitioning and clustering approaches, in turn influencing future work. Furthermore, it offers valuable insights for researchers working in the domain of clustering and data analytics.</p>