Clustering analysis is the task of assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than to those in other clusters. Clustering analysis is the major application area of data mining where Particle Swarm Optimisation (PSO) is being widely implemented due to its simplicity and efficiency. When compared with techniques like K-means, Fuzzy C-means, K-Harmonic means and other traditional clustering approaches, in general, the PSO algorithm produces better results with reference to inter-cluster and intra-cluster distances, while having quantization errors comparable to the other algorithms. In recent times, many hybrid algorithms with PSO as one of the techniques have been developed to harness the strong points of PSO and increase its efficiency and accuracy. This paper provides an extensive review of the variants and hybrids of PSO which are being widely used for the purpose of clustering analysis.