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Fuzzy Linguistic Summaries via Association Rules

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Data Mining and Computational Intelligence

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 68))

Abstract

In this contribution, we discuss how a fuzzy querying interface can support the generation of linguistic database summaries — a special technique of data mining. Links between our approach to linguistic summaries and the well-known technique of association rules is shown. The generation of linguistic summaries is implemented by using the authors’ FQUERY for Access package.

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References

  1. Agrawal R. and Srikant R., “Fast algorithms for mining association rules”, in: Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 1994.

    Google Scholar 

  2. Anwar T.M., Beck H.W. and Navathe S.B., “Knowledge mining by imprecise querying: A classification based system”, in: Proceedings of the International Conference on Data Engineering, Tampa, USA, 1992, 622–630.

    Google Scholar 

  3. Bosc P. and J. Kacprzyk (eds.), Fuzziness in Database Management Systems. Physica-Verlag, Heidelberg, 1995.

    MATH  Google Scholar 

  4. Bosc P. and O. Pivert, “Fuzzy querying in conventional databases”. In: L.A. Zadeh and J. Kacprzyk (eds.): Fuzzy Logic for the Management of Uncertainty. Wiley, New York, 1992, 645–671.

    Google Scholar 

  5. Bosc P., L. Lietard and O. Pivert, “Quantified statements and database fuzzy querying, in P. Bosc and J. Kacprzyk (eds.): Fuzziness in Database Management Systems. Physica-Verlag, Heidelberg, 1995, 275–308.

    Google Scholar 

  6. George R. and R. Srikanth, “Data summarization using genetic algorithms and fuzzy logic”, in: F. Herrera and J.L. Verdegay (eds.): Genetic Algorithms and Soft Computing. Physica-Verlag, Heidelberg and New York, 1996, 599–611.

    Google Scholar 

  7. Kacprzyk J. and R.R. Yager (2000) “Linguistic summaries of data using fuzzy logic”. International Journal of General Systems (in press)

    Google Scholar 

  8. Kacprzyk J., R.R. Yager, and S. Zadrozny (2000) “Fuzzy linguistic summaries of databases for an efficient business data analysis and decision support”. In W. Abramowicz and J. Zurada (Eds.) „Selected Aspects and New Trends in Knowledge Discovery for Business Information Systems. Kluwer, Boston (in press)

    Google Scholar 

  9. Kacprzyk J., R.R. Yager, and S. Zadrozny (2000) “A fuzzy logic based approach to linguistic summaries of databases”. International Journal of Applied Mathematics and Computer Science (in press)

    Google Scholar 

  10. Kacprzyk J. and S. Zadrozny, “FQUERY for Access: fuzzy querying for a Windows-based DBMS”, in: P. Bosc and J. Kacprzyk (eds.) Fuzziness in Database Management Systems, Physica-Verlag, Heidelberg, 1995, 415–43 3.

    Google Scholar 

  11. Kacprzyk J. and S. Zadrozny, “Fuzzy queries in Microsoft Access v. 2.”, in: D. Dubois, H. Prade and R.R. Yager (eds.): Fuzzy Information Engineering–A Guided Tour of Applications, Wiley, New York, 1997, 223–232.

    Google Scholar 

  12. Kacprzyk J. and S. Zadrozny, “Implementation of OWA operators in fuzzy querying for Microsoft Access”, in: R.R. Yager and J. Kacprzyk (eds.) The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer, Boston 1997, 293–306.

    Chapter  Google Scholar 

  13. Kacprzyk J. and S. Zadrozny, “Flexible querying using fuzzy logic: An implementation for Microsoft Access”, in: T. Andreasen, H. Christiansen and H.L. Larsen (eds.): Flexible Query Answering Systems, Kluwer, Boston, 1997, 247–275.

    Chapter  Google Scholar 

  14. Kacprzyk J. and S. Zadrozny, “Data mining via linguistic summaries of data: An interactive approach”, in T. Yamakawa and G. Matsumoto (eds.): Methodologies for the Conception, Design and Application of Soft Computing (Proceedings of IIZUKA’98), Iizuka, Japan, 1998, 668–671.

    Google Scholar 

  15. Kacprzyk J. and S. Zadrozny, “On sumarization of large datasets via a fuzzy-logic-based querying add-on to Microsoft Access”, in: Intelligent Information Systems VII (Malbork, Poland), IPI PAN, Warsaw, 1998, 249–258.

    Google Scholar 

  16. Kacprzyk J. and Ziólkowski A., “Database queries with fuzzy linguistic quantifiers”, IEEE Transactions on Systems, Man and Cybernetics SMC -16, 1986, 474–479.

    Google Scholar 

  17. Lee J.-H. and Lee-Kwang H., “An extension of association rules using fuzzy sets”, in: Proceedings of the Seventh IFSA World Congress, 1997, Prague, Czech Republic. Vol. 1, 399–402.

    Google Scholar 

  18. Mannila H., Toivonen H. and Verkamo A.I., “Efficient algorithms for discovering association rules”, in: U.M. Fayyad and R. Uthurusamy (eds.) Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, Seattle, USA, 1994, 181–192.

    Google Scholar 

  19. Miller R.J. and Yang Y., “Association rules over interval data”. in: Proceedings of the ACM SIGMOD International Conference on the Management of Data, Tucson, USA, 1997, 452–461.

    Google Scholar 

  20. Petry F.E. Fuzzy Databases: Principles and Applications. Kluwer, Boston, 1996.

    Book  MATH  Google Scholar 

  21. Rasmussen D. and R.R. Yager (1997) Fuzzy query language for hypothesis evaluation, in Andreasen T., H. Christiansen and H. L. Larsen (eds.) Flexible Query Answering Systems. Kluwer, Boston/Dordrecht/London, 2343.

    Google Scholar 

  22. Srikant R. and Agrawal R., “Mining generalized association rules”, in: Proceedings of the 215t International Conference on Very Large Databases”, Zurich, Switzerland, 1995.

    Google Scholar 

  23. Srikant R. and Agrawal R., “Mining quantitative association rules in large relational tables”, in: Proceedings of the ACM-SIGMOD 1996 Conference on Management of Data, Montreal, Canada, 1996.

    Google Scholar 

  24. Srikant R., Vu Q. and R. Agrawal, “Mining association rules with item constraints”, in: Proceedings of the 3nd International Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, USA, 1997.

    Google Scholar 

  25. Vila M.A., Cubero J.C., Medina J.M. and Pons O., “Logic and fuzzy relational databases: a new language and a new definition”, in P. Bosc and J. Kacprzyk (eds.) Fuzziness in Database Management Systems. PhysicaVerlag, Heidleberg, 1994, 114–138.

    Google Scholar 

  26. Weber S., “A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms”, Fuzzy Sets and Systems 11, 1983, 115–134.

    Article  MathSciNet  MATH  Google Scholar 

  27. Yager R.R., “On linguistic summaries of data”, in: G. Piatetsky-Shapiro and W.J. Frawley (eds.) Knowledge Discovery in Databases. AAAI Press/The MIT Press, Menlo Park, 1991, 347–363.

    Google Scholar 

  28. Zemankova M. and J. Kacprzyk, “The roles of fuzzy logic and management of uncertainty in building intelligent information systems”, Journal of Intelligent Information Systems 2, 1993, 311–317.

    Article  Google Scholar 

  29. Zadeh L.A., “A computational approach to fuzzy quantifiers in natural languages”, Computers and Maths. with Appls. 9, 1983, 149–184.

    Article  MathSciNet  MATH  Google Scholar 

  30. Zadeh L.A.,”A computational theory of dispositions”, International Journal of Intelligent Systems 2, 1987, 39–64.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, ul. Newelska 6, Poland

    Janusz Kacprzyk & Slawomir Zadrożny

Authors
  1. Janusz Kacprzyk
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  2. Slawomir Zadrożny
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave., ENB 118, 33620, Tampa, Florida, USA

    Abraham Kandel

  2. Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel

    Mark Last

  3. Department of Computer Science, University of Bern, Neubruckstrasse 10, CH-3012, Bern, Switzerland

    Horst Bunke

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© 2001 Springer-Verlag Berlin Heidelberg

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Kacprzyk, J., Zadrożny, S. (2001). Fuzzy Linguistic Summaries via Association Rules. In: Kandel, A., Last, M., Bunke, H. (eds) Data Mining and Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 68. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1825-3_5

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  • DOI: https://doi.org/10.1007/978-3-7908-1825-3_5

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2484-1

  • Online ISBN: 978-3-7908-1825-3

  • eBook Packages: Springer Book Archive

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