Abstract
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.
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Cocx, T.K., Kosters, W.A., Laros, J.F.J. (2008). Temporal Extrapolation within a Static Clustering. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_21
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DOI: https://doi.org/10.1007/978-3-540-68123-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68122-9
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