Comparative analysis of clustering techniques in cloud for effective load balancing

Abstract

Clustering is used as an important procedure in the process of data mining, where information of large datasets are transformed into meaningful and concise data. It performs activities like pattern representation, using of clustering algorithms and their validation, data abstraction and finally result generated. Clustering has many categories of algorithms such as partition-based, hierarchical-based, density-based, grid-based etc. Partition-based is the centroid-based clustering. Hierarchical-based clustering is link-based. Density-based is clustering is focused on area of higher density in the dataset. Grid-based clustering relies on size of the grid. In this paper, we discussed different clustering techniques as well as, a detailed review on the partition-based and hierarchical-based algorithms. Finally we compare clustering algorithms on the basis of attributes like time complexity, capacity of handling large datasets, scalability, sensitivity to outliers and noise, and also discussed result after solving a particular dataset implemented in cloud computing environment.