Improved BSP Clustering Algorithm for Social Network Analysis
Journal Title: Bonfring International Journal of Software Engineering and Soft Computing - Year 2012, Vol 1, Issue 1
Abstract
Social network analysis is a new research field in data mining. Social network analysis is the study of social networks to recognize the structure and behavior of friends. Social network analysis views social relationships in terms of network theory consisting of nodes and ties. The defining feature of social network analysis is its focus on the structure of relationships, ranging from casual acquaintance to close bonds. Social network analysis assumes that relationships are very essential. The main aspect of the social network analysis is clustering. The clustering in social network analysis is different from conventional clustering techniques. It needs grouping objects into classes depending on their links as well as their attributes. The conventional clustering approaches group objects only based on objects? similarity and it cannot be applied to social network analysis. So on the basis of BSP (Business System Planning) clustering algorithm, a social network clustering analysis algorithm is proposed. Moreover, before applying clustering, the Principal Component Analysis (PCA) technique is applied. Thus proposed algorithm, different from traditional BSP clustering algorithms, can group objects in a social network into different classes based on their links and identify relation among classes dynamically & require less amount of memory.
Authors and Affiliations
Saranya Preethi B
Keywords
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- EP ID EP156473
- DOI 10.9756/BIJSESC.1003
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