Extraction, Analysis, and Visualization of Temporal Association Rules from Interval-Based Clinical Data

  • Carlo Combi
  • Alberto Sabaini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


Temporal association rules have been recently applied to interval-based temporal clinical data, to discover complex temporal relationships. In this paper, we first propose a refinement of the Data-Mining algorithm proposed by Sacchi et al. (2007) for the extraction of temporal association rules, improving the algorithm complexity in case of anti-monotonous rule support. Then, we address the non-trivial problem of displaying and visually analyzing this kind of data, through the use of an OLAP-based multidimensional model, and by proposing a visualization solution explicitly dealing with temporal association rules.


Temporal Fact Mining Algorithm Precedence Constraint Left Shift Temporal Support 
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 2013

Authors and Affiliations

  • Carlo Combi
    • 1
  • Alberto Sabaini
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaItaly

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