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Fuzzy Methods for Data Mining and Machine Learning: State of the Art and Prospects

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Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

Methods for the automated induction of models and the extraction of interesting patterns from empirical data have recently attracted considerable attention in the fuzzy set community. This chapter briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to machine learning, data mining, and related fields. Finally, a critical consideration of recent developments is given and some suggestions regarding future research are made.

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References

  1. R. Babuska. Fuzzy Modeling for Control. Kluwer Academic Publ., Boston, 1998.

    Google Scholar 

  2. H. Bandemer and W. Näther. Fuzzy Data Analysis. Kluwer Academic Publishers, Dordrecht, 1992.

    MATH  Google Scholar 

  3. C. Borgelt and R. Kruse. Graphical Models – Methods for Data Analysis and Mining. Wiley, Chichester, 2002.

    MATH  Google Scholar 

  4. L. Breiman. Bagging predictors. Machine Learning, 24(2)123–140, 1996.

    MATH  MathSciNet  Google Scholar 

  5. J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, editors. Interpretability Issues in Fuzzy Modeling. Springer-Verlag, Berlin, 2003.

    MATH  Google Scholar 

  6. G. Chen, Q. Wei, E. Kerre, and G. Wets. Overview of fuzzy associations mining. In Proc. ISIS–2003. Jeju, Korea, September 2003.

    Google Scholar 

  7. Y. Chen and JZ. Wang. Support vector learning for fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 11(6)716–728, 2003.

    Article  Google Scholar 

  8. O. Cordon, MJ. del Jesus, and F. Herrera. Analyzing the reasoning mechanisms in fuzzy rule based classification systems. Mathware & Soft Computing, 5:321–332, 1998.

    MATH  Google Scholar 

  9. O. Cordon, F. Gomide, F. Herrera, and F. Hoffmann anf L. Magdalena. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems, 141(1)5–31, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  10. TH. Dang, B. Bouchon-Meunier, and C. Marsala. Measures of information for inductive learning. In Proc. IPMU-2004, Perugia, Italy, 2004.

    Google Scholar 

  11. M. Delgado, N. Marin, D. Sanchez, and M. Vila. Fuzzy association rules: general model and applications. IEEE Trans. on Fuzzy Syst., 11(2)214–225, 2003.

    Article  Google Scholar 

  12. T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer Theory. IEEE Transa. on Systems, Man, and Cybernetics, 25(5)804–813, 1995.

    Article  Google Scholar 

  13. P. Diamond and P. Kloeden. Metric Spaces of Fuzzy Sets: Theory and Applications. World Scientific, Singapur, 1994.

    MATH  Google Scholar 

  14. P. Diamond and H. Tanaka. Fuzzy regression analysis. In R. Slowinski, editor, Fuzzy Sets in Decision Analysis, Operations Research and Statistics, pp. 349–387. Kluwer, 1998.

    Google Scholar 

  15. TG. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, First International Workshop on Multiple Classifier Systems, Vol. 1857, pp. 1–15. Springer-Verlag, 2000.

    Google Scholar 

  16. D. Dubois, F. Esteva, P. Garcia, L. Godo, R. Lopez de Mantaras, and H. Prade. Fuzzy set modelling in case-based reasoning. International Journal of Intelligent Systems, 13:345–373, 1998.

    Article  MATH  Google Scholar 

  17. D. Dubois, E. Hüllermeier, and H. Prade. Fuzzy set-based methods in instance-based reasoning. IEEE Transactions on Fuzzy Systems, 10(3)322–332, 2002.

    Article  Google Scholar 

  18. D. Dubois, E. Hüllermeier, and H. Prade. A note on quality measures for fuzzy association rules. Proc. IFSA–03, pp. 677–648, Istambul, July 2003.

    Google Scholar 

  19. D. Dubois and H. Prade. What are fuzzy rules and how to use them. Fuzzy Sets and Systems, 84:169–185, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  20. Z. Elouedi, K. Mellouli, and P. Smets. Decision trees using the belief function theory. In Proc. IPMU-2000, Vol. I, pp. 141–148, Madrid, 2000.

    Google Scholar 

  21. UM. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining. MIT Press, 1996.

    Google Scholar 

  22. Y. Freund and RE. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning, pp. 148–156, 1996.

    Google Scholar 

  23. J. Fürnkranz and E. Hüllermeier. Pairwise preference learning and ranking. In Proc. ECML–2003, Cavtat-Dubrovnik, Croatia, 2003. Springer-Verlag.

    Google Scholar 

  24. AP. Gasch and MB. Eisen. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology, 3(11)1–22, 2002.

    Google Scholar 

  25. J. Gebhardt and R. Kruse. A possibilistic interpretation of fuzzy sets by the context model. In IEEE Intern. Conf. on Fuzzy Systems, pp. 1089–1096, 1992.

    Google Scholar 

  26. F. Höppner, F. Klawonn, F. Kruse, and T. Runkler. Fuzzy Cluster Analysis. Wiley, Chichester, 1999.

    MATH  Google Scholar 

  27. E. Hüllermeier. Implication-based fuzzy association rules. Proc. PKDD–01, pp. 241–252, Freiburg, Germany, 2001. Springer-Verlag.

    Google Scholar 

  28. E. Hüllermeier. Possibilistic induction in decision tree learning. Proc. ECML–02, pp. 173–184, Helsinki, Finland, 2002. Springer-Verlag.

    Google Scholar 

  29. E. Hüllermeier. Possibilistic instance-based learning. Artificial Intelligence, 148(1–2): 335–383, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  30. E. Hüllermeier. Cho-k-NN: A method for combining interacting pieces of evidence in case-based learning. Proc. IJCAI–05, pp. 3–8, Edinburgh, 2005.

    Google Scholar 

  31. E. Hüllermeier, I. Renners, and A. Grauel. An evolutionary approach to constraint-regularized learning. Mathware & Soft Comp., 11:109–124, 2004.

    MATH  Google Scholar 

  32. CZ. Janikow. Fuzzy decision trees: Issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, 28(1)1–14, 1998.

    Google Scholar 

  33. R. Krishnapuram and J.M. Keller. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2)98–110, 1993.

    Article  Google Scholar 

  34. R. Kruse and C. Borgelt. Information mining: Editorial. Int. Journal of Approximate Reasoning, 32:63–65, 2003.

    Article  Google Scholar 

  35. A. Laurent. Generating fuzzy summaries: a new approach based on fuzzy multidimensional databases. Intelligent Data Analysis Journal, 7(2)155–177, 2003.

    MATH  Google Scholar 

  36. S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, 50(2)157–224, 1988.

    MATH  MathSciNet  Google Scholar 

  37. T.M. Mitchell. The need for biases in learning generalizations. Technical Report TR CBM–TR–117, Rutgers University, 1980.

    Google Scholar 

  38. D. Nauck, F. Klawonn, and R. Kruse. Foundations of Neuro-Fuzzy Systems. Wiley and Sons, Chichester, UK, 1997.

    Google Scholar 

  39. C. Olaru and L. Wehenkel. A complete fuzzy decision tree technique. Fuzzy Sets and Systems, 138(2), 2003.

    Google Scholar 

  40. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA, 1988.

    Google Scholar 

  41. E.H. Ruspini. Possibility as similarity: The semantics of fuzzy logic. In P.P. Bonissone, H. Henrion, L.N. Kanal, and J.F. Lemmer, editors, Uncertainty In Artificial Intelligence 6. Elsevier Science Publisher, 1991.

    Google Scholar 

  42. B. Schölkopf and AJ. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.

    Google Scholar 

  43. T. Sudkamp. Similarity as a foundation for possibility. In Proc. 9th IEEE Int. Conference on Fuzzy Systems, pp. 735–740, San Antonio, 2000.

    Google Scholar 

  44. T. Sudkamp. Examples, counterexamples, and measuring fuzzy associations. Fuzzy Sets and Systems, 149(1), 2005.

    Google Scholar 

  45. LG. Valiant. A theory of the learnable. CACM, 17(11)1134–1142, 1984.

    Google Scholar 

  46. V.N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, second edition, 2000.

    MATH  Google Scholar 

  47. R. Viertl. Statistical Methods for Non-Precise Data. CRC Press, Boca Raton, Florida, 1996.

    Google Scholar 

  48. R. Weber. Fuzzy-ID3: a class of methods for automatic knowledge acquisition. In Proc. IIZUKA-92, Vol. 1, pp. 265–268. 1992.

    Google Scholar 

  49. R.R. Yager. Soft aggregation methods in case based reasoning. Applied Intelligence, 21:277–288, 2004.

    Article  MATH  Google Scholar 

  50. L. Zadeh. New Approach to the Analysis of Complex Systems. IEEE Trans. Syst., Man, Cybern., SMC, 3(1), 1973.

    Google Scholar 

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Authors
  1. Eyke Hüllermeier
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Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

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Hüllermeier, E. (2008). Fuzzy Methods for Data Mining and Machine Learning: State of the Art and Prospects. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_18

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  • DOI: https://doi.org/10.1007/978-3-540-73723-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

  • eBook Packages: EngineeringEngineering (R0)

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