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Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps

  • Puteri N. E. Nohuddin
  • Rob Christley
  • Frans Coenen
  • Yogesh Patel
  • Christian Setzkorn
  • Shane Williams
Conference paper

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.

Keywords

Social Network Trend Line Frequent Pattern Time Stamp Support Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.School of Veterinary ScienceUniversity of Liverpool and National Centre for Zoonosis ResearchLeahurstUK
  3. 3.Deeside Insurance Ltd.DeesideUK

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