Interactive Analysis of Preference-Ordered Data Using Dominance-Based Rough Set Approach

  • Jerzy Błaszczyński
  • Krzysztof Dembczyński
  • Roman Słowiński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


We present a method of interactive analysis of preference ordered data that is based on Dominance-based Rough Set Approach (DRSA). The presented here methodology is conceptually similar to multi-dimensional reports (pivot tables) applied in On-Line Analytical Processing (OLAP). However, it allows to identify patterns in data that remain undiscovered by traditional approaches to multi-dimensional reporting. The main difference consists in use of specific dimensions and measures defined within DRSA. The method permits to find a set of reports that ensures specified properties of analyzed data and is optimal with respect to a given criterion. An example of reports generated for a well-known breast cancer data set is included.


Interactive Analysis Decision Table Decision Class Wisconsin Breast Cancer Pivot Table 


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  1. 1.
    Błaszczyński, Dembczyński, K.J., Słowiński, R.: On-Line Satisfaction Analysis using Dominance-based Rough Set Approach. In: Rutkowska, D., et al. (eds.) Selected Problems of Computer Science (2005)Google Scholar
  2. 2.
    Dembczyński, K., Greco, S., Słowiński, R.: Second-order Rough Approximations in Multi-criteria Classification with Imprecise Evaluations and Assignments. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 54–63. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Greco, S., Matarazzo, B., Słowiński, R.: A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. (ed.) Operational Tools in the Management of Financial Risks, pp. 121–136. Kluwer, Dordrecht (1998)Google Scholar
  4. 4.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough approximation of a preference relation by dominance relations. European Journal of Operational Research 117, 63–83 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, 1–47 (2001)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research 138, 247–259 (2002)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Kohavi, R., Sommerfield, D.: Targeting Business Users with Decision Table Classifiers. In: Knowledge Discovery and Data Mining, pp. 249–253 (1998)Google Scholar
  8. 8.
    Michalski, R.S.: A Planar Geometrical Model for Representing Multi-Dimensional Discrete Spaces and Multiple-Valued Logic Functions. In: ISG Report No. 897, Department of Computer Science, University of Illinois, Urbana (1978)Google Scholar
  9. 9.
    R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna (2005),
  10. 10.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Irvine, CA: University of California, Department of Information and Computer Science (1998),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jerzy Błaszczyński
    • 1
  • Krzysztof Dembczyński
    • 1
  • Roman Słowiński
    • 1
    • 2
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland
  2. 2.Institute for Systems ResearchPolish Academy of SciencesWarsawPoland

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