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Fuzzy model generation using Subtractive and Fuzzy C-Means clustering

  • Special Issue REDSET 2016 of CSIT
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Abstract

Clustering is a process of partitioning similar data into groups. For this, number of clustering algorithms have been proposed in literature. Some of them can also be used for the generation of Fuzzy Models. In this work, Sugeno fuzzy models being generated by Subtractive and FCM clustering have been discussed. Experiments have been performed on real datasets to compare the Subtractive and FCM clustering. Further, the effect of increase in the radius size is analyzed in Subtractive clustering. The average absolute error and root mean square error is also found out when using FCM clustering and Subtractive clustering with different values of radius.

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References

  1. Hammouda K, Karray F (2000) A comparative study of data clustering techniques. Fakhreddine Karray University of Waterloo, Ontario

    Google Scholar 

  2. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MathSciNet  MATH  Google Scholar 

  3. Polo J (2001) Development and testing of a number of MATLAB based fuzzy system applications. http://deeea.urv.cat/public/PROPOSTES/pub/pdf/120pub.pdf. Accessed 12 Aug 2016

  4. Kim K, Kim YK, Kim E, Park M (2004) A new TSK fuzzy modeling approach. In: Proceedings of 2004 IEEE International Conference on Fuzzy Systems, 2004, IEEE, vol 2, pp 773–776

  5. Siler W, Buckley JJ (2005) Fuzzy expert systems and fuzzy reasoning. Wiley, Hoboken, New Jersey

    MATH  Google Scholar 

  6. Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377

    Article  Google Scholar 

  7. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River, NJ

  8. Zimmermann HJ (1994) Fuzzy set theory-and its applications. Kluwer Academic Publishers

  9. Polat K (2016) Intelligent recognition of diabetes disease via FCM based attribute weighting. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inform Eng 10(4):676–680

    Google Scholar 

  10. Bataineh KM, Naji M, Saqer M (2011) A comparison study between various fuzzy clustering algorithms. Jordan J Mech Ind Eng 5(4):335–343

    Google Scholar 

  11. Nayak J, Naik B, Behera HS (2015) Fuzzy C-Means (FCM) clustering algorithm: a decade review from 2000 to 2014. Comput Intell Data Min 2:133–149 Springer India

    Google Scholar 

  12. Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy C-means algorithms. IJACSA Int J Adv Comput Sci Appl 4(4):35–39

    Google Scholar 

  13. Yazdani-Chamzini A, Razani M, Yakhchali SH, Zavadskas EK, Turskis Z (2013) Developing a fuzzy model based on subtractive clustering for road header performance prediction. Autom Constr 35:111–120

    Article  Google Scholar 

  14. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Lalit Mohan Goyal

  2. Department of Computer Engineering, G. B. Pant Government Engineering College, Okhla, New Delhi, India

    Mamta Mittal

  3. Research Scholar, USICT, GGSIPU, New Delhi, India

    Jasleen Kaur Sethi

Authors
  1. Lalit Mohan Goyal
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  2. Mamta Mittal
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  3. Jasleen Kaur Sethi
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Correspondence to Lalit Mohan Goyal.

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Goyal, L.M., Mittal, M. & Sethi, J.K. Fuzzy model generation using Subtractive and Fuzzy C-Means clustering. CSIT 4, 129–133 (2016). https://doi.org/10.1007/s40012-016-0090-3

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  • DOI: https://doi.org/10.1007/s40012-016-0090-3

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