Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.


Attribute Reduction Decision Table Decision Attribute Conditional Attribute Discernibility Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Marek, W., Pawlak, Z.: Rough Set and Information Systems. Fundamenta Informaticae 17, 105–115 (1984)MathSciNetGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)MATHGoogle Scholar
  4. 4.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial. In: Rough Fuzzy Hybridization –A New Trend in Decision Making, pp. 3–98. Springer, Heidelberg (1998)Google Scholar
  5. 5.
    Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information System, Intelligent Decision Support-Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  6. 6.
    Hoa, N.S., Son, N.H.: Some efficient algorithms for rough set methods. In: Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems (1996)Google Scholar
  7. 7.
    Hu, Q., Pao, W., Yu, D.: Improved reduction algorithm based on A-Priori. Computer Science 29, 115–117 (2002)Google Scholar
  8. 8.
    Best, J.B.: Cognitive Psychology. Heinle and Heinle Publishers, Boston (1998)Google Scholar
  9. 9.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)Google Scholar
  10. 10.
    Wang, J., Wang, J.: Reduction algorithms based on discernibility matrix: The ordered attributes method. Journal of Computer Science and Technology 16, 489–504 (2001)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuguo He
    • 1
  1. 1.Department of Computer Science and EngineeringBeijing Institute of TechnologyBeijingChina

Personalised recommendations