Comparative study of several Clustering Algorithms

Cluster Analysis is a process of grouping the objects, where objects can be physical like a student or can be an abstract such as behaviour of a customer or handwriting of a person. The cluster analysis is as old as a human life and has its roots in many fields such as statistics, machine learning, biology, artificial intelligence. It is an unsupervised learning and faces many challenges such as a high dimension of the dataset, arbitrary shapes of clusters, scalability, input parameter, domain knowledge and noisy data. Large number of clustering algorithms had been proposed till date to address these challenges. There do not exist a single algorithm which can adequately handle all sorts of requirement. This makes a great challenge for the user to do selection among the available algorithm for the specific task. The purpose of this paper is to provide a detailed analytical comparison of some of the very well known clustering algorithms, which provides guidance for the selection of clustering algorithm for a specific application.