Visualisation of Trend Pattern Migrations in Social Networks

  • Puteri N. E. NohuddinEmail author
  • Frans Coenen
  • Rob Christley
  • Wataru Sunayama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)


In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of a Migration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.


Trend analysis Trend clustering Visualisation Self organising maps Frequent patterns 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Puteri N. E. Nohuddin
    • 1
    Email author
  • Frans Coenen
    • 2
  • Rob Christley
    • 3
  • Wataru Sunayama
    • 4
  1. 1.Institute of Visual InformaticsUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  3. 3.School of Veterinary ScienceUniversity of Liverpool and National Centre for Zoonosis ResearchNestonUK
  4. 4.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan

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