Clustering is a division of data into groups of similar objects. Each group is called as a cluster. Data in the same cluster are similar and to the other are dissimilar the aim of this paper is intended to study and compare different clustering algorithms. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The measures of similarity as well as the evaluation criteria, which are the central components of clustering are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.