Skip to main content
SpringerLink
Log in
Book cover

Data Mining and Knowledge Discovery Handbook pp 505–520Cite as

Using Fuzzy Logic in Data Mining

  • Chapter
  • First Online:

Summary

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   349.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • 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 

  • Arbel, R. and Rokach, L., Classifier evaluation under limited resources, Pattern Recognition Letters, 27(14): 1619–1631, 2006, Elsevier.

    Article  Google Scholar 

  • 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 

  • J. C. Bezdek. Fuzzy Mathematics in Pattern Classification. PhD Thesis, Applied Math. Center, Cornell University, Ithaca, 1973.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  MATH  Google Scholar 

  • Jang J., ”Structure determination in fuzzy modeling: A fuzzy CART approach,” in Proc. IEEE Conf. Fuzzy Systems, 1994, pp. 480485.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • S. Mitra and S. K. Pal, Fuzzy sets in pattern recognition and machine intelligence, Fuzzy Sets and Systems 156 (2005) 381386

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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 

  • 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. 1044-1053

    Google Scholar 

  • Olaru C., Wehenkel L., A complete fuzzy decision tree technique, Fuzzy Sets and Systems, 138(2):221–254, 2003.

    Article  MathSciNet  Google Scholar 

  • Peng Y., Intelligent condition monitoring using fuzzy inductive learning, Journal of Intelligent Manufacturing, 15 (3): 373-380, June 2004.

    Article  Google Scholar 

  • Rokach, L., Decomposition methodology for classification tasks: a meta decomposer framework, Pattern Analysis and Applications, 9(2006):257–271.

    Article  MathSciNet  Google Scholar 

  • Rokach L., Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008.

    Article  MATH  Google Scholar 

  • Rokach L., Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78, 2008.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321–352, 2005, Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications,World Scientific Publishing, 2008.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • Yuan Y., Shaw M., Induction of fuzzy decision trees, Fuzzy Sets and Systems 69(1995):125-139.

    Article  MathSciNet  Google Scholar 

  • Zimmermann H. J., Fuzzy Set Theory and its Applications, Springer, 4th edition, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information System Engineering, Ben-Gurion University, Beer-Sheva, Israel

    Lior Rokach

Authors
  1. Lior Rokach
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lior Rokach .

Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

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

    Lior Rokach

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Rokach, L. (2009). Using Fuzzy Logic in Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_24

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-09823-4_24

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation