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Mining Patterns from Longitudinal Studies

  • Aída Jiménez
  • Fernando Berzal
  • Juan-Carlos Cubero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)

Abstract

Longitudinal studies are observational studies that involve repeated observations of the same variables over long periods of time. In this paper, we propose the use of tree pattern mining techniques to discover potentially interesting patterns within longitudinal data sets. Following the approach described in [15], we propose four different representation schemes for longitudinal studies and we analyze the kinds of patterns that can be identified using each one of the proposed representation schemes. Our analysis provides some practical guidelines that might be useful in practice for exploring longitudinal datasets.

Keywords

Representation Scheme Longitudinal Data Analysis Longitudinal Dataset Sibling Node Longitudinal Database 
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 2011

Authors and Affiliations

  • Aída Jiménez
    • 1
  • Fernando Berzal
    • 1
  • Juan-Carlos Cubero
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceCITIC, University of GranadaGranadaSpain

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