Analysis of Cluster Migrations Using Self-Organizing Maps

  • Denny
  • Peter Christen
  • Graham J. Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)


Discovering cluster changes in real-life data is important in many contexts, such as fraud detection and customer attrition analysis. Organizations can use such knowledge of change to adapt business strategies in response to changing circumstances. This paper is aimed at the visual exploration of migrations of cluster entities over time using Self-Organizing Maps. The contribution is a method for analyzing and visualizing entity migration between clusters in two or more snapshot datasets. Existing research on temporal clustering primarily focuses on either time-series clustering, clustering of sequences, or data stream clustering. There is a lack of work on clustering snapshot datasets collected at different points in time. This paper explores cluster changes between such snapshot data. Besides analyzing structural cluster changes, analysts often desire deeper insight into changes at the entity level, such as identifying which attributes changed most significantly in the members of a disappearing cluster. This paper presents a method to visualize migration paths and a framework to rank attributes based on the extent of change among selected entities. The method is evaluated using synthetic and real-life datasets, including data from the World Bank.


temporal cluster analysis cluster migration analysis visual data exploration change analysis Self-Organizing Map 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burez, J., den Poel, D.V.: CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications 32(2), 277–288 (2007)CrossRefGoogle Scholar
  2. 2.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 1(2), 224–227 (1979)CrossRefGoogle Scholar
  3. 3.
    Denny, Squire, D.M.: Visualization of Cluster Changes by Comparing Self-Organizing Maps. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 410–419. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Denny, Williams, G.J., Christen, P.: Exploratory Hot Spot Profile Analysis using Interactive Visual Drill-down Self-Organizing Maps. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 536–543. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Denny, Williams, G.J., Christen, P.: Visualizing Temporal Cluster Changes using Relative Density Self-Organizing Maps. Knowledge and Information Systems 25, 281–302 (2010)CrossRefGoogle Scholar
  6. 6.
    Diggle, P.J., Liang, K.Y., Zeger, S.L.: Analysis of Longitudinal Data. Oxford University Press, New York (1994)zbMATHGoogle Scholar
  7. 7.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Lingras, P., Hogo, M., Snorek, M.: Temporal cluster migration matrices for web usage mining. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 441–444. IEEE Computer Society (2004)Google Scholar
  9. 9.
    Moore, D.S.: The basic practice of Statistics. W H. Freemand and Company, New York (2000)Google Scholar
  10. 10.
    Reinartz, W.J., Kumar, V.: The impact of customer relationship characteristics on profitable lifetime duration. The Journal of Marketing 67(1), 77–99 (2003)CrossRefGoogle Scholar
  11. 11.
    Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14(4), 750–767 (2002)CrossRefGoogle Scholar
  12. 12.
    Seear, M.: An Introduction to International Health. Canadian Scholars’ Press Inc., Toronto (2007)Google Scholar
  13. 13.
    Serrano-Cinca, C.: Let financial data speak for themselves. In: Deboeck, G., Kohonen, T. (eds.) Visual Explorations in Finance with Self-Organizing Maps, pp. 3–23. Springer, London (1998)CrossRefGoogle Scholar
  14. 14.
    Vesanto, J.: SOM-based data visualization methods. Intelligent Data Analysis 3(2), 111–126 (1999)CrossRefzbMATHGoogle Scholar
  15. 15.
    Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  16. 16.
    World Bank: World Development Indicators 2003. The World Bank, Washington DC (2003)Google Scholar
  17. 17.
    Zagha, R., Nankani, G.T. (eds.): Economic Growth in the 1990s: Learning from a Decade of Reform. World Bank Publications, Washington, DC (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Denny
    • 1
    • 2
  • Peter Christen
    • 1
  • Graham J. Williams
    • 1
    • 3
  1. 1.Research School of Computer ScienceThe Australian National UniversityCanberraAustralia
  2. 2.Faculty of Computer ScienceUniversity of IndonesiaIndonesia
  3. 3.Australian Taxation OfficeCanberraAustralia

Personalised recommendations