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High Frequent Value Reduct in Very Large Databases

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4482))

Abstract

One of the main contributions of rough set theory to data mining is data reduction. There are three reductions: attribute (column) reduction, row reduction, and value reduction. Row reduction is merging the duplicate rows. Attribute reduction is to find important attributes. Value reduction is to reduce the decision rules to a logically equivalent minimal length. Most recent attentions have been on finding attribute reducts. Traditionally, the value reduct has been searched through the attribute reduct. This paper observes that this method may miss the best value reducts. It also revisits an old rudiment idea [11], namely, a rough set theory on high frequency data: The notion of high frequency value reduct is extracted in a bottom-up fashion without finding attribute reducts. Our method can discover concise and important decision rules in large databases, and is described and illustrated by an example.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD Conference, pp. 207–216 (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  3. Chen, R., Lin, T.Y.: Supporting Rough Set Theory in Very Large Database Using ORACLE RDBMS. In: Soft Computing in Intelligent Systems and Information Processing, Proceedings of 1996 Asian Fuzzy Systems Symposium, Kenting, Taiwan, December 11-14, 1996, pp. 332–337 (1996)

    Google Scholar 

  4. Chen, R., Lin, T.Y.: Finding Reducts in Very Large Databases. In: Proceedings of Joint Conference of Information Science, Research Triangle Park, North Carolina, March 1-5, 1997, pp. 350–352 (1997)

    Google Scholar 

  5. Fernandez-Baizan, M., Ruiz, E., Wasilewska, A.: A Model of RSDM Implementation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 186–193. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  6. Garcia-Molina, H., Ullman, J., Widom, J.: Database Systems: The Complete Book. Prentice-Hall, Englewood Cliffs (2001)

    Google Scholar 

  7. Han, J., Hu, X., Lin, T.: A new computation model for rough set theory based on database systems. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 381–390. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Houtsma, M., Swami, A.: Set-Oriented Mining for Association Rules in Relational Databases. In: Proc. of Int. Conf. on Data Engineering, pp. 25–33 (1995)

    Google Scholar 

  9. Hu, X., Lin, T., Han, J.: A new rough sets model based on database systems. J. of Fundamenta Informaticae 59(2-3), 135–152 (2004)

    MathSciNet  MATH  Google Scholar 

  10. Lin, T.Y.: Neighborhood Systems and Approximation in Database and Knowledge Base Systems. In: Proceedings of the Fourth International Symposium on Methodologies of Intelligent Systems, Poster Session, October 12-15, 1989, pp. 75–86 (1989)

    Google Scholar 

  11. Lin, T.Y.: Rough Set Theory in Very Large Database Mining. In: Symposium on Modeling, Analysis and Simulation, CESA’96 IMACS Multi Conference (Computational Engineering in Systems Applications), vol. 2, Lille, France, July 9-12, 1996, pp. 936–994 (1996)

    Google Scholar 

  12. Pawlak, Z.: Rough Sets. International Journal of Information and Computer Science 11(5), 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  13. Pawlak, Z.: Rough sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  14. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Decision Support by Experience - Application of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA 95192, USA

    Tsau Young Lin

  2. Department of Computer Science, California State University Dominguez Hills, Carson, CA 90747, USA

    Jianchao Han

Authors
  1. Tsau Young Lin
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  2. Jianchao Han
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, York University, M3J 1P3, Toronto, Ontario, Canada

    Aijun An

  2. Institute of Computing Sciences, Poznań University of Technology, ul. Piotrowo 2, 60–965, Poznań, Poland

    Jerzy Stefanowski

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Cory J. Butz

  5. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

  6. Institute of Compuer Science and Technology, Chongqing University of Posts and Telecommunications, 40065, Chongqing, P.R. China

    Guoyin Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Lin, T.Y., Han, J. (2007). High Frequent Value Reduct in Very Large Databases. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_41

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  • DOI: https://doi.org/10.1007/978-3-540-72530-5_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72529-9

  • Online ISBN: 978-3-540-72530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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