Abstract
A technique for identifying, grouping and analysing trends in social networks is described. The trends of interest are defined in terms of sequences of support values for specific patterns that appear across a given social network. The trends are grouped using a SOM technique so that similar tends are clustered together. A cluster analysis technique is then applied to identify “interesting” trends. The focus of the paper is the Cattle Tracing System (CTS) database in operation in Great Britain, and this is therefore the focus of the evaluation. However, to illustrate the wider applicability of the trend mining technique, experiments using a more standard, car insurance, temporal database are also described.
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Nohuddin, P.N.E., Christley, R., Coenen, F., Patel, Y., Setzkorn, C., Williams, S. (2011). Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_24
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DOI: https://doi.org/10.1007/978-0-85729-130-1_24
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