Improved BSP Clustering Algorithm for Social Network Analysis

Abstract

A social network is a social structure of individuals, who are linked (directly or indirectly to each other) depending on a common relation of interest, e.g. friendship, trust, etc. Social network analysis is the study of social networks to recognize the structure and behavior of social actor. Social network analysis has gained importance due to its usage in different applications - from product marketing to search engines and organizational dynamics. The conventional clustering approaches group objects based only on objects' similarity which does not suite for social network data. Social network objects should be grouped into classes depending on their links as well as their attributes. In this paper, a clustering algorithm based on BSP (Business System Planning) clustering with Principal Component Analysis (PCA) technique is proposed. This algorithm produces significant improvement in clusters, as it groups objects in a social network into different classes based on their links and identify relation among classes.