Using Fuzzy Logic in Data Mining

  • Lior RokachEmail author


In this chapter we discuss how fuzzy logic extends the envelop of the main data mining tasks: clustering, classification, regression and association rules. We begin by presenting a formulation of the data mining using fuzzy logic attributes. Then, for each task, we provide a survey of the main algorithms and a detailed description (i.e. pseudo-code) of the most popular algorithms. However this chapter will not profoundly discuss neuro-fuzzy techniques, assuming that there will be a dedicated chapter for this issue.


Data Mining Membership Function Fuzzy Logic Association Rule Fuzzy Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. R. Agrawal, T. Imielinski and A. Swami: Mining Association Rules between Sets of Items in Large Databases. Proceeding of ACM SIGMOD, 207-216. Washington, D.C, 1993.Google Scholar
  2. Arbel, R. and Rokach, L., Classifier evaluation under limited resources, Pattern Recognition Letters, 27(14): 1619–1631, 2006, Elsevier.CrossRefGoogle Scholar
  3. Averbuch, M. and Karson, T. and Ben-Ami, B. and Maimon, O. and Rokach, L., Contextsensitive medical information retrieval, The 11th World Congress on Medical Informatics (MEDINFO 2004), San Francisco, CA, September 2004, IOS Press, pp. 282–286.Google Scholar
  4. J. C. Bezdek. Fuzzy Mathematics in Pattern Classification. PhD Thesis, Applied Math. Center, Cornell University, Ithaca, 1973.Google Scholar
  5. Cios K. J. and Sztandera L. M., Continuous ID3 algorithm with fuzzy entropy measures, Proc. IEEE lnternat. Con/i on Fuzz)’ Systems,1992, pp. 469-476.Google Scholar
  6. Cohen S., Rokach L., Maimon O., Decision Tree Instance Space Decomposition with Grouped Gain-Ratio, Information Science, Volume 177, Issue 17, pp. 3592-3612, 2007.CrossRefGoogle Scholar
  7. T.P. Hong, C.S. Kuo and S.C. Chi: A Fuzzy Data Mining Algorithm for Quantitative Values. 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings. IEEE 1999, pp. 480-3.Google Scholar
  8. T.P. Hong, C.S. Kuo and S.C. Chi: Mining Association Rules from Quantitative Data. Intelligent Data Analysis, vol.3, no.5, nov. 1999, pp363-376.zbMATHCrossRefGoogle Scholar
  9. Jang J., ”Structure determination in fuzzy modeling: A fuzzy CART approach,” in Proc. IEEE Conf. Fuzzy Systems, 1994, pp. 480485.Google Scholar
  10. Janikow, C.Z., Fuzzy Decision Trees: Issues and Methods, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 28, Issue 1, pp. 1-14. 1998.Google Scholar
  11. Kim, J., Krishnapuram, R. and Dav, R. (1996). Application of the Least Trimmed Squares Technique to Prototype-Based Clustering, Pattern Recognition Letters, 17, 633-641.CrossRefGoogle Scholar
  12. Joseph Komem and Moti Schneider, On the Use of Fuzzy Logic in Data Mining, in The Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach (Eds.), pp. 517-533, Springer, 2005.Google Scholar
  13. Maher P. E. and Clair D. C, Uncertain reasoning in an ID3 machine learning framework, in Proc. 2nd IEEE Int. Conf. Fuzzy Systems, 1993, pp. 712.Google Scholar
  14. Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311–336, 2001.Google Scholar
  15. Maimon O. and Rokach L., “Improving supervised learning by feature decomposition”, Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp. 178-196, 2002.Google Scholar
  16. Maimon, O. and Rokach, L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, Series in Machine Perception and Artificial Intelligence - Vol. 61, World Scientific Publishing, ISBN:981-256-079-3, 2005.Google Scholar
  17. S. Mitra, Y. Hayashi, ”Neuro-fuzzy Rule Generation: Survey in Soft Computing Framework.” IEEE Trans. Neural Networks, Vol. 11, N. 3, pp. 748-768, 2000.CrossRefGoogle Scholar
  18. S. Mitra and S. K. Pal, Fuzzy sets in pattern recognition and machine intelligence, Fuzzy Sets and Systems 156 (2005) 381386CrossRefMathSciNetGoogle Scholar
  19. Moskovitch R, Elovici Y, Rokach L, Detection of unknown computer worms based on behavioral classification of the host, Computational Statistics and Data Analysis, 52(9):4544–4566, 2008.zbMATHCrossRefMathSciNetGoogle Scholar
  20. Nasraoui, O. and Krishnapuram, R. (1997). A Genetic Algorithm for Robust Clustering Based on a Fuzzy Least Median of Squares Criterion, Proceedings of NAFIPS, Syracuse NY, 217-221.Google Scholar
  21. Nauck D., Neuro-Fuzzy Systems: Review and Prospects Paper appears in Proc. Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT’97), Aachen, Sep. 8-11, 1997, pp. 1044-1053Google Scholar
  22. Olaru C., Wehenkel L., A complete fuzzy decision tree technique, Fuzzy Sets and Systems, 138(2):221–254, 2003.CrossRefMathSciNetGoogle Scholar
  23. Peng Y., Intelligent condition monitoring using fuzzy inductive learning, Journal of Intelligent Manufacturing, 15 (3): 373-380, June 2004.CrossRefGoogle Scholar
  24. Rokach, L., Decomposition methodology for classification tasks: a meta decomposer framework, Pattern Analysis and Applications, 9(2006):257–271.CrossRefMathSciNetGoogle Scholar
  25. Rokach L., Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008.zbMATHCrossRefGoogle Scholar
  26. Rokach L., Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78, 2008.CrossRefGoogle Scholar
  27. Rokach L., Maimon O. and Lavi I., Space Decomposition In Data Mining: A Clustering Approach, Proceedings of the 14th International Symposium On Methodologies For Intelligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 24–31.Google Scholar
  28. Rokach, L. and Maimon, O. and Averbuch, M., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag, 2004.Google Scholar
  29. Rokach, L. and Maimon, O. and Arbel, R., Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20 (3)(2006), pp. 329–350.CrossRefGoogle Scholar
  30. Rokach, L. and Maimon, O., Theory and applications of attribute decomposition, IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 473–480, 2001.Google Scholar
  31. Rokach L. and Maimon O., Feature Set Decomposition for Decision Trees, Journal of Intelligent Data Analysis, Volume 9, Number 2, 2005b, pp 131–158.Google Scholar
  32. Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321–352, 2005, Springer.Google Scholar
  33. Rokach, L. and Maimon, O., Data mining for improving the quality of manufacturing: a feature set decomposition approach, Journal of Intelligent Manufacturing, 17(3):285–299, 2006, Springer.CrossRefGoogle Scholar
  34. Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications,World Scientific Publishing, 2008.Google Scholar
  35. E. Shnaider and M. Schneider, Fuzzy Tools for Economic Modeling. In: Uncertainty Logics: Applications in Economics and Management. Proceedings of SIGEF’98 Congress, 1988.Google Scholar
  36. Shnaider E., M. Schneider and A. Kandel, 1997, A Fuzzy Measure for Similarity of Numerical Vectors, Fuzzy Economic Review, Vol. II, No. 1, 1997, pp. 17 -Google Scholar
  37. Tani T. and Sakoda M., Fuzzy modeling by ID3 algorithm and its application to prediction of heater outlet temperature, Proc. IEEE lnternat. Conf. on Fuzzy Systems, March 1992, pp. 923-930.Google Scholar
  38. Yuan Y., Shaw M., Induction of fuzzy decision trees, Fuzzy Sets and Systems 69(1995):125-139.CrossRefMathSciNetGoogle Scholar
  39. Zimmermann H. J., Fuzzy Set Theory and its Applications, Springer, 4th edition, 2005.Google Scholar

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Information System EngineeringBen-Gurion UniversityBeer-ShevaIsrael

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