Temporal Extrapolation within a Static Clustering

  • Tim K. Cocx
  • Walter A. Kosters
  • Jeroen F. J. Laros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4994)


Predicting the behaviour of individuals is a core business of policy makers. This paper discusses a new way of predicting the “movement in time” of items through pre-defined classes by analysing their changing placement within a static, preconstructed 2-dimensional clustering. It employs the visualization realized in previous steps within item analysis, rather than performing complex calculations on each attribute of each item. For this purpose we adopt a range of well-known mathematical extrapolation methods that we adapt to fit our need for 2-dimensional extrapolation. Usage of the approach on a criminal record database to predict evolvement of criminal careers, shows some promising results.


Static Cluster Extrapolation Method Multi Dimensional Scaling Criminal Career Item Behaviour 
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 Berlin Heidelberg 2008

Authors and Affiliations

  • Tim K. Cocx
    • 1
  • Walter A. Kosters
    • 1
  • Jeroen F. J. Laros
    • 1
  1. 1.Leiden Institute of Advanced Computer ScienceLeiden UniversityThe Netherlands

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