Clustering plays a vital role in research area in the field of data mining. Clustering is a process of partitioning a set of data in a meaningful sub classes called clusters. It helps users to understand the natural grouping or cluster from the data set. It is unsupervised classification that means it has no predefined classes. This paper presents a study of various partitioning techniques of clustering algorithms and their relative study by reflecting their advantages individually. Applications of cluster analysis are Economic Science, Document classification, Pattern Recognition, Image Processing, text mining. No single algorithm is efficient enough to crack problems from different fields. Hence, in this study some algorithms are presented which can be used according to one's requirement. In this paper, various well known partitioning based methods – k-means, k-medoids and Clarans – are compared. The study given here explores the behaviour of these three methods.

Clustering k-means k-medoids Clarans