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Tree Expressions for Information Systems

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Abstract

The discernibility matrix is one of the most important approaches to computing positive region, reduct, core and value reduct in rough sets. The subject of this paper is to develop a parallel approach of it, called “tree expression”. Its computational complexity for positive region and reduct is O(m 2 × n) instead of O(m × n 2) in discernibility-matrix-based approach, and is not over O(n 2) for other concepts in rough sets, where m and n are the numbers of attributes and objects respectively in a given dataset (also called an “information system” in rough sets). This approach suits information systems with n ≫ m and containing over one million objects.

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References

  1. Pawlak Z. Rough Set—Theoretical Aspects of Reasoning About Data. Dorderecht, Kluwer Academic Publishers, 1991.

    Google Scholar 

  2. Wang J, Zhao M, Zhao K, Han S. Multilevel data summarization from information system: A “rule + exception” approach, AI Communications, 2003, 16(1): 17–39.

    MATH  MathSciNet  Google Scholar 

  3. Wang F. Intelligence and security informatics: An emerging interdisciplinary field based on computational intelligence. International Journal of Intelligent Control and Systems, 2003, 18(4): 476–483.

    Google Scholar 

  4. Hu X, Cercone N. Learning in relational databases: A rough set approach. International Journal of Computational Intelligence, 1995, 11(2): 323–338.

    Article  Google Scholar 

  5. Miao D, Wang J. Information-based algorithm for reduction of knowledge. In Proc. IEEE ICIPS’97, Beijing, 1997, pp.1155–1158.

  6. Wróblewski J. Finding minimal reducts using genetic algorithms. In Proc. the Second Annual Join Conference on Information Sciences, 1995, pp.186–189.

  7. Skowron A, Rauszer C. The discernibility matrices and functions in information systems. Intelligent Decision Support—Handbook of Applications and Advances of the Rough Sets Theory, Slowinski R (ed.), Kluwer Academic Publishers, 1992, pp.331–362.

  8. Wang J, Wang J. Reduction algorithms based on discernibility matrix: The ordered attributes method. Journal of Computer Science and Technology, 2001, 16(6): 489–504.

    MATH  MathSciNet  Google Scholar 

  9. Zhao M. Data description based on reduct theory [Dissertation]. Institute of Automation, Chinese Academy of Sciences, 2004.

  10. Quinlan J. Induction of decisions trees. Machine Learning, 1986, 1: 81–106.

    Google Scholar 

  11. Ye D, Chen Z. Inconsistency classification and discernibility—Matrix-based approaches for computing an attribute core. In Proc. RSFDGrC, Chongqing, China, 2003, pp.269–273.

  12. Han S, Wang J. Reduct and attribute order. J. Computer Science and Technology, 2004, 19(4): 429–449.

    Article  MathSciNet  Google Scholar 

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Correspondence to Min Zhao.

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This work is partially supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2004CB318103 and the National Nature Science Foundation of China under Grant No. 60573078.

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Zhao, M., Han, SQ. & Wang, J. Tree Expressions for Information Systems. J Comput Sci Technol 22, 297–307 (2007). https://doi.org/10.1007/s11390-007-9037-3

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  • DOI: https://doi.org/10.1007/s11390-007-9037-3

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