Indiscernibility and Similarity in an Incomplete Information Table

  • Renpu Li
  • Yiyu Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)


A new method is proposed for interpreting and constructing relationships between objects in an incomplete information table. An incomplete information table is expressed as a family of complete tables. One can define an indiscernibility relation for each complete table and then get a family of indiscernibility relations of all complete tables. A pair of an indiscernibility and a similarity relation is constructed by the intersection and union of this family of indiscernibility relation. It provides a clear semantic interpretation for relationship between objects of an incomplete information table. In fact, it is a pair of bounds of the actual indiscernibility relation if all values in the incomplete table were known.


Incomplete information table completion indiscernibility relation similarity relation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Guan, Y.: Set-valued information systems. Information Sciences 176, 2507–2525 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Grzymala-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W., Yao, Y. (eds.) Proceedings of the Second International Conference on Rough Sets and Current Trends in Computing, pp. 378–385. Physica-Verlag, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Jaworski, W.: Generalized indiscernibility relations: applications for missing values and analysis of structural objects. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 116–145. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Sciences 112, 39–49 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113, 271–292 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Zhao, Y., Yao, Y.Y., Luo, F.: Data analysis based on discernibility and indiscernibility. Information Sciences 177, 4959–4976 (2007)zbMATHCrossRefGoogle Scholar
  7. 7.
    Leung, Y., Li, D.: Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences 153, 85–106 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Witold Lipski, J.R.: On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems 4, 262–296 (1979)CrossRefGoogle Scholar
  9. 9.
    Nakamura, A.: A rough logic based on incomplete information and its applicaton. International Journal of Approximate Reasoning 15, 367–378 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Orlowska, E.: Introduction: what you always wanted to know about rough sets. In: Orlowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 1–20. Physica-Verlag, Heidelberg (1998)Google Scholar
  11. 11.
    Pawlak, Z.: Information systems theoretical foundations. Information Systems 6, 205–218 (1981)zbMATHCrossRefGoogle Scholar
  12. 12.
    Wang, G.: Extension of rough set under incomplete information systems. Journal of Computer Research and Development 39, 1238–1243 (2002)Google Scholar
  13. 13.
    Zhang, W.: Incomplete information system and its optimal selections. Computers and Mathematics with Applications 48, 691–698 (2004)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Renpu Li
    • 1
    • 2
  • Yiyu Yao
    • 2
  1. 1.School of Information Science and TechnologyLudong University, YantaiChina
  2. 2.Department of Computer ScienceUniversity of Regina, ReginaSaskatchewanCanada

Personalised recommendations