A Tentative Approach to Minimal Reducts by Combining Several Algorithms

  • Ning Xu
  • Yunxiang Liu
  • Ruqi Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

Abstract

Finding minimal reducts is a NP-hard problem. For obtain a feasible solution, depth-first-searching is mainly used and a feasible reduct always can be gotten. Whether the feasible reduct is a minimal reduct or not and how far it is to minimal reduct, both are not known. It only gives the information that how many attributes it has and it is a reduct. Based on rough sets reduction theory and the data structure of information system, the least condition attributes to describe the system’s classified characteristics can be known. So an area of searching minimal reducts is decided. By binary search in the area, the minimal reducts can be gotten quickly and doubtlessly.

Keywords

rough sets algorithm attribute reduction minimal reduct 

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References

  1. 1.
    Pawlak, Z.: Rough Sets, Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets and Their Applications. Microcomputer Applications 13(2), 71–75 (1994)Google Scholar
  3. 3.
    Wong, S.K.M., Ziarko, W.: On Optimal Decision Rules in Decision Tables. Bullet. Polish Acad. Sci. 33, 693–696 (1995)MathSciNetGoogle Scholar
  4. 4.
    Xu, N.: The Theory and Technique Research of Attribute Reduction in Data Mining Based on Rough Sets, PhD dissertation, Guangdong University of Technology (2005)Google Scholar
  5. 5.
    Ni, Z., Cai, J.: Discrete Mathematics. Science Publishes (2002)Google Scholar
  6. 6.
    Zhang, W., Wu, W., Liang, J., Li, D.: Theory and Method of Rough Sets. Science Publishes (2001)Google Scholar
  7. 7.
    Guo, J.: Rough set-based approach to data mining, PhD dissertation, Department of Electrical Engineering and Computer Science, Case Wester University, USA (2003)Google Scholar
  8. 8.
    Hu, X.: Knowledge Discovery in Database: An Attribute-oriented Rough Set Approach (Rules, Decision Matrices), PhD dissertation, The University of Regina, Canada (995)Google Scholar
  9. 9.
    Wang, J., Miao, D.: Analysis on Attribute Reduction Strategies of Rough Set. J. Comput. Sci. Technol. 13(2), 189–193 (1998)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Shi, Z.: Knowledge Discovery. Tsinghua University Press, Beijing (2002)Google Scholar
  11. 11.
    Duntsch, I., Gediga, G., Orlowska, E.: Relation Attribute Systems II: Reasoning with Relations in Information Structures. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 16–35. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ning Xu
    • 1
    • 2
  • Yunxiang Liu
    • 1
  • Ruqi Zhou
    • 2
  1. 1.School of Computer Science and Information EngineeringShanghai Institute of TechnologyShanghaiChina
  2. 2.Dept. of Computer ScienceGuangdong Institute of EducationGuangzhouChina

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