Abstract
Social CRM is critical in utilities services provided by cloud computing. These services rely on virtual customer communities forming spontaneously and evolving continuously. Thus clarifying the explicit boundaries of these communities is quite essential to the quality of utilities services in cloud computing. Communities with overlapping feature or projecting vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from a CR networks. These uneven shapes usually play a prominent role in finding prominent customer which is usually ignored in social CRM. A novel method of discovering irregular community based on density threshold and similarity degree. It finds and merges primitive maximal cliques from the first. Irregular features of overlapping and prominent sparse vertex are further considered. An empirical case and a method comparison test indicates its efficiency and feasibility.
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Liu, J., Liu, F., Zhou, J., He, C. (2009). Irregular Community Discovery for Social CRM in Cloud Computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds) Cloud Computing. CloudCom 2009. Lecture Notes in Computer Science, vol 5931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10665-1_45
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DOI: https://doi.org/10.1007/978-3-642-10665-1_45
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