Knowledge Reduction Based on Granular Computing from Decision Information Systems

  • Lin Sun
  • Jiucheng Xu
  • Shuangqun Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)


Efficient knowledge reduction in large inconsistent decision information systems is a challenging problem. Moreover, existing approaches have still their own limitations. To address these problems, in this article, by applying the technique of granular computing, provided some rigorous and detailed proofs, and discussed the relationship between granular reduct introduced and knowledge reduction based on positive region related to simplicity decision information systems. By using radix sorting and hash methods, the object granules as basic processing elements were employed to investigate knowledge reduction. The proposed method can be applied to both consistent and inconsistent decision information systems.


Granular computing Rough set theory Knowledge reduction Decision information systems Granular 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  2. 2.
    Miao, D.Q., Wang, G.Y., Liu, Q., Lin, T.Y., Yao, Y.Y.: Granular Computing: Past, Present, and the Future Perspectives. Academic Press, Beijing (2007)Google Scholar
  3. 3.
    Xu, J.C., Sun, L.: New Reduction Algorithm Based on Decision Power of Decision Table. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 180–188. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Xu, J.C., Sun, L.: Research of Knowledge Reduction Based on New Conditional Entropy. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 144–151. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Yao, Y.Y.: A Partition Model of Granular Computing. LNCS Transactions on Rough Sets 1, 232–253 (2004)CrossRefGoogle Scholar
  6. 6.
    Lin, T.Y., Louie, E.: Finding Association Rules by Granular Computing: Fast Algorithms for Finding Association Rules. In: Proceedings of the 12th International Conference on Data Mining, Rough Sets and Granular Computing, Berlin, German, pp. 23–42 (2002)Google Scholar
  7. 7.
    Kryszkiewicz, M.: Comparative Study of Alternative Types of Knowledge Reduction in Insistent Systems. International Journal of Intelligent Systems 16, 105–120 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood Classifiers. Expert Systems with Applications 34, 866–876 (2008)CrossRefGoogle Scholar
  9. 9.
    Xu, Z.Y., Liu, Z.P., et al.: A Quick Attribute Reduction Algorithm with Complexity of Max(O(|C||U|),O(|C| 2|U/C|)). Journal of Computers 29(3), 391–399 (2006)Google Scholar
  10. 10.
    Liu, Y., Xiong, R., Chu, J.: Quick Attribute Reduction Algorithm with Hash. Chinese Journal of Computers 32(8), 1493–1499 (2009)Google Scholar
  11. 11.
    Liu, S.H., Sheng, Q.J., Wu, B., et al.: Research on Efficient Algorithms for Rough Set Methods. Chinese Journal of Computers 26(5), 524–529 (2003)Google Scholar
  12. 12.
    Guan, J.W., Bell, D.A.: Rough Computational Methods for Information Systems. International Journal of Artificial Intelligences 105, 77–103 (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lin Sun
    • 1
  • Jiucheng Xu
    • 1
  • Shuangqun Li
    • 1
  1. 1.College of Computer and Information TechnologyHenan Normal UniversityXinxiang HenanChina

Personalised recommendations