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Issue No. 01 - Jan.-Feb. (2013 vol. 17)
ISSN: 1089-7801
pp: 74-79
Maria Giatsoglou , Aristotle University
Athena Vakali , Aristotle University
The fast and unpredictable evolution of social data poses challenges for capturing user activities and complex associations. Evolving social graph clustering promises to uncover the dynamics of latent user and content patterns. This Web extra overviews evolving data clustering approaches.
Data models, Clustering algorithms, Complexity theory, Internet, Adaptation models, mining methods and algorithms, Web mining, database applications, database management, clustering, pattern recognition, graphs and networks, data structures, information search and retrieval

M. Giatsoglou and A. Vakali, "Capturing Social Data Evolution Using Graph Clustering," in IEEE Internet Computing, vol. 17, no. , pp. 74-79, 2013.
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