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Extended Random Sets for Knowledge Discovery in Information Systems

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

In this paper, we discuss the problem of knowledge discovery in information systems. A model is presented for users to obtain either “objective” interesting rules or “subjective” judgments of meaningful descriptions based on their needs. Extended random sets are presented firstly to describe the relationships between condition granules and decision granules. The interpretation is then given to show what we can obtain from the extended random sets.

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References

  1. R. Agrawal, T. Imielinski and A. Swami, Database mining: a performance perspective, IEEE Transactions on Knowledge and Data Engineering, 1993, 5(6):914–925.

    Article  Google Scholar 

  2. M.-S. Chen, J. Han, and P. S. Yu, Data mining: an overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6):866–883.

    Article  Google Scholar 

  3. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy, eds., Advances in knowledge discovery and data mining, Menlo Park, California: AAAI Press/The MIT Press, 1996.

    Google Scholar 

  4. S. Greco, Z. Pawlak, and R. Slowinski, Generalized decision algorithm, rough inference rules, and flow graphs, 3 rd International Conference on Rough Sets and Current Trends in Computing, Malvern, PA, USA, 2002, 93–104.

    Google Scholar 

  5. R. Kruse, E. Schwecke and J. Heinsoln, Uncertainty and vagueness in knowledge based systems (Numerical Methods), Springer-Verlag, New York, 1991.

    MATH  Google Scholar 

  6. T. Y. Lin, The lattice structure of database and mining multiple level rules, Bulletin of International Rough Set Society, 2002, Vol 6 No 1/2, pp 11–16.

    Google Scholar 

  7. D. Liu and Y. Li, The interpretation of generalized evidence theory, Chinese Journal of Computers, 1997, 20(2):158–164.

    Google Scholar 

  8. Z. Pawlak, In pursuit of patterns in data reasoning from data — the rough set way, 3 rd International Conference on Rough Sets and Current Trends in Computing, Malvern, PA, USA, 2002, 1–9.

    Google Scholar 

  9. G. Shafer, A mathematical theory of evidence, Princeton University Press, Princeton, NJ, 1976.

    MATH  Google Scholar 

  10. P. Walley, Measures of uncertainty in expert systems, Artificial Intelligence, 1996, 83: 1–58.

    Article  MathSciNet  Google Scholar 

  11. J. Y. Yao and Y. Y. Yao, Induction of classification rules by granular computing, 3 rd International Conference on Rough Sets and Current Trends in Computing, Malvern, PA, USA, 2002, 331–338.

    Google Scholar 

  12. Y. Y. Yao, On modelling data mining with granular computing, Proceedings of COMPASAC, 2001, 638–643.

    Google Scholar 

  13. Y. Y. Yao, S. K. M. Wong and T.Y. Lin, A review of rough set models, in: Rough sets and data mining, edited by T.Y. Lin and N. Cercone, Kluwer Academic Publishers, Boston, 1997, 47–75.

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Li, Y. (2003). Extended Random Sets for Knowledge Discovery in Information Systems. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_87

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  • DOI: https://doi.org/10.1007/3-540-39205-X_87

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

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