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


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.


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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  1. 1.
  2. 2.
    Streibel, O.: Trend Mining with Semantic-Based Learning. Proceedings of CAiSE-DC (2008)Google Scholar
  3. 3.
    Khan, M.S., Coenen, F., Reid, D., Tawfik, H., Patel, R., Lawson, A.: A SlidingWindows based Dual Support Framework for Discovering Emerging Trends from Temporal Data. Research and Development in Intelligent Systems XXVIl, Springer, London, pp 35-48 (2010)CrossRefGoogle Scholar
  4. 4.
    Raza, J. and Liyanage, J. P.: An integrated qualitative trend analysis approach to identify process abnormalities: a case of oil export pumps in an offshore oil and gas production facility. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Professional Engineering Publishing, vol 223 (4), pp 251-258 (2008)CrossRefGoogle Scholar
  5. 5.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (2006)Google Scholar
  6. 6.
    Lauw, H., Lim, E., Pang, H., Tan T.: Social Network Discovery by Mining Spatio-Temporal Events. Computational Mathematical Organization Theory, vol 11(2), pp. 97-118. Springer Netherlands (2005)Google Scholar
  7. 7.
    Agrawal, R., Imielinski, T., and Swami, A. Mining Association Rules between Sets of Items in Large Databases. In Proceedings of ACM SIGMOD Conference (1993)Google Scholar
  8. 8.
    Agrawal, R. andSrikant, R.: Mining sequential patterns. 11th International Conference on Data Engineering (1995)Google Scholar
  9. 9.
    Mannila, H., Toivonen, H., and Verkamo, A.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1, pp 259289(1997)Google Scholar
  10. 10.
    Dong, G., and Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In Proceeding of fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1999)Google Scholar
  11. 11.
    Coenen, F.P., Goulbourne, G., Leng, P.: Computing Association Rules Using Partial Totals. Principles of Data Mining and Knowledge Discovery. LNCS, vol. 2168, pp. 54-66. Springer Berlin / Heidelberg (2001)Google Scholar
  12. 12.
    Kohonen, T.: The Self Organizing Maps. Neurocomputing Elsevier Science, vol. 21, pp. 1-6 (1998)MATHCrossRefGoogle Scholar
  13. 13.
    Kohonen, T.: The Self Organizing Maps. Series in Information Sciences, vol. 30. Springer, Heidelberg (1995)Google Scholar
  14. 14.
    Wang, J., Delabie, J., Aasheim, H.C., Smel, E., Myklebost, O.: Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics, vol 3(36) (2002)Google Scholar
  15. 15.
    Yan, S., Abidi, S.S.R, Artes, P.H.: Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images. Studies in Health Technology and Informatics, vol 116 pp 483-488 (2005)Google Scholar
  16. 16.
    Cottrell, M., Rousset, P.: A powerful Tool for Analyzing and Representing Multidimensional Quantitative and Qualitative Data. In Proceedings of IWANN 97. LNCS, vol. 1240, pp. 861-871. Springer Berlin / Heidelberg (1997)Google Scholar
  17. 17.
    Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the Self-Organizing Map. Proceedings of the IEEE, vol. 84(10), pp. 1358-1384 (1996)CrossRefGoogle Scholar
  18. 18.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (2006)Google Scholar
  19. 19.
    Lingras, P., Hogo, M. and Snorek, M.: Temporal Cluster Migration Matrices for Web Usage Mining. In Proceedings of IEEE/WIC/ACM InternationalConference on Web Intelligence (2004)Google Scholar
  20. 20.
    Denny, Williams, G.J and Christen, P.: ReDSOM: relative density visualization of temporal changes in cluster structures using self-organizing maps. IEEE International Conference on Data Mining (ICDM), IEEE Computer Society, pp 173-182 (2008)Google Scholar
  21. 21.
    Hido, S., Id T., Kashima, H., Kubo H. and Matsuzawa, H.: Unsupervised changes analysis using supervised learning. Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference. PAKDD. LNCS, vol. 5012, pp 148-159 (2008)Google Scholar

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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