Possibilistic Data Analysis and Its Similarity to Rough Sets

  • Hideo Tanaka
  • Peijun Guo
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 95)


This paper is dealing with the upper and lower approximation models for representing the given phenomenon in a fuzzy environment. Based on the given data, the upper and lower approximation models can be derived from upper and lower directions, respectively where the inclusion relationship between these two models holds. Thus, the inherent fuzziness existing in the given phenomenon can be represented by the upper and lower models. The modalities of the upper and lower models have been illustrated in regression analysis and also in the identification methods of possibility distributions. The comparison of the concepts of possibility data analysis and rough sets is shown. A measure similar to the accuracy measure of rough sets is used to clarify the difference between the data structure and the assumed model.


Constraint Condition Linear Programming Problem Portfolio Selection Inclusion Relation Possibility Distribution 
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 2002

Authors and Affiliations

  • Hideo Tanaka
    • 1
  • Peijun Guo
    • 2
  1. 1.Graduate School of Management and InformationToyohashi Sozo CollegeToyohashi-shiJapan
  2. 2.Faculty of EconomicsKagawa UniversityTakamatsu, KagawaJapan

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