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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Concaro, S., Sacchi, L., Cerra, C., Fratino, P., Bellazzi, R.: Mining healthcare data with temporal association rules: Improvements and assessment for a practical use. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS, vol. 5651, pp. 16–25. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Flatla, D.R., Gutwin, C.: Individual models of color differentiation to improve interpretability of information visualization. In: SIGCHI, pp. 2563–2572 (2010)Google Scholar
  3. 3.
    Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: A conceptual model for data warehouses. Int. J. Cooperative Inf. Syst. 7(2-3), 215–247 (1998)CrossRefGoogle Scholar
  4. 4.
    Lin, W., Orgun, M.A., Williams, G.: An overview of temporal data mining. In: Proceedings of the 1st Australian Data Mining Workshop (AusDM 2002), pp. 83–90. University of Technology, Sydney (2002)Google Scholar
  5. 5.
    Sacchi, L., Larizza, C., Combi, C., Bellazzi, R.: Data mining with temporal abstractions: learning rules from time series. Data Mining Knowledge Discovery 15(2), 217–247 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison–Wesley (2005)Google Scholar
  7. 7.
    Techapichetvanich, K., Datta, A.: VisAR: A new technique for visualizing mined association rules. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 88–95. Springer, Heidelberg (2005)CrossRefGoogle Scholar

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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