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Inference Processes Using Incomplete Knowledge in Decision Support Systems – Chosen Aspects

  • Agnieszka Nowak-Brzezińska
  • Tomasz Jach
  • Alicja Wakulicz-Deja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)

Abstract

The authors propose to use cluster analysis techniques (particularly clustering) to speed-up the process of finding rules to be activated in complex decision support systems with incomplete knowledge. The authors also wish to inference within such decision support systems using rules, of which premises are not fully covered by the facts. The AHC or mAHC algorithm is used. The authors adapted Salton’s most promising path method with own modifications for a fast look-up of the rules.

Keywords

knowledge bases cluster analysis clustering decision support systems incomplete knowledge inference AHC 

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References

  1. 1.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefGoogle Scholar
  2. 2.
    Salton, G.: Automatic Information Organization and Retreival. McGraw-Hill, New York (1975)Google Scholar
  3. 3.
    Wakulicz-Deja, A., Nowak-Brzezińska, A., Jach, T.: Inference Processes in Decision Support Systems with Incomplete Knowledge. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 616–625. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Simiński, R., Nowak-Brzezińska, A., Jach, T., Xięski, T.: Towards a Practical Approach to Discover Internal Dependencies in Rule-Based Knowledge Bases. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 232–237. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Jain, A., Dubes, R.: Algorithms for clustering data. Prentice Hall (1988)Google Scholar
  6. 6.
    Koronacki, J., Ćwik, J.: Statystyczne systemy uczące się. Exit, Warszawa (2008)Google Scholar
  7. 7.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. UC, SoIaCS, Irvine, CA (2010), http://archive.ics.uci.edu/ml Google Scholar
  8. 8.
    Myatt, G.: Making Sense of Data. A Practical Guide to Exploratory Data Analysis and Data Mining. John Wiley and Sons, Inc., New Jersey (2007)Google Scholar
  9. 9.
    Kumar, V., Tan, P., Steinbach, M.: Introduction to Data Mining. Addison-Wesley (2006)Google Scholar
  10. 10.
    Pawlak, Z.: Rough set approach to knowledge-based decision suport. European Journal of Operational Research, 48–57 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Agnieszka Nowak-Brzezińska
    • 1
  • Tomasz Jach
    • 1
  • Alicja Wakulicz-Deja
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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