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Enhancement of existing clustering algorithm for tracking online community in social network

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. The increasing achievement of the Web has led people to exploit collaborative technologies in order to encourage partnerships among different groups. The problem of tracking community in social networks inferred from online interactions by tracking evolution of known subgroups over time. Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. The focus of present paper is enhancement of clustering methods for tracking online community interaction.