Pawlak Rough Set Model, Medical Reasoning and Rule Mining

  • Shusaku Tsumoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


This paper overviews the following two important issues on the correspondence between Pawlak’s rough set model and medical reasoning. The first main idea of rough sets is that a given concept can be approximated by partition-based knowledge as upper and lower approximation. Interestingly, thes approximations correspond to the focusing mechanism of differential medical diagnosis; upper approximation as selection of candidates and lower approximation as concluding a final diagnosis. The second idea of rough sets is that a concept, observations can be represented as partitions in a given data set, where rough sets provides a rule induction method from a given data. Thus, this model can be used to extract rule-based knowledge from medical databases. Especially, rule induction based on the focusing mechanism is obtained in a natural way.


Rule Mining Venn Diagram Medical Reasoning Target Concept Positive Rule 
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 2006

Authors and Affiliations

  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane University, School of MedicineIzumoJapan

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