Skip to main content
SpringerLink
Log in

Differential Evolution Based Fuzzy Clustering

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

Included in the following conference series:

Abstract

In this work, two new fuzzy clustering (FC) algorithms based on Differential Evolution (DE) are proposed. Five well-known data sets viz. Iris, Wine, Glass, E. Coli and Olive Oil are used to demonstrate the effectiveness of DEFC-1 and DEFC-2. They are compared with Fuzzy C-Means (FCM) algorithm and Threshold Accepting Based Fuzzy Clustering algorithms proposed by Ravi et al., [1]. Xie-Beni index is used to arrive at the ‘optimal’ number of clusters. Based on the numerical experiments, we infer that, in terms of least objective function value, these variants can be used as viable alternatives to FCM algorithm.

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

Access this chapter

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 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ravi, V., Bin, M., Ravi Kumar, P.: Threshold Accepting based fuzzy clustering algorithms. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 14(5), 617–632 (2006)

    Article  MATH  Google Scholar 

  2. Bezdek, J.C.: Fuzzy Mathematics in Pattern Classification, Ph. D. thesis, Center for applied mathematics, Cornell University (1973)

    Google Scholar 

  3. Kuncheva, L.I., Bezdek, J.C.: Selection of cluster prototypes from data by a genetic algorithm. In: Proc. 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT), Aachen, Germany, vol. 18, pp. 1683–1688 (1997)

    Google Scholar 

  4. Sun, L.-X., Danzer, K.: Fuzzy cluster analysis by simulate annealing. Journal of Chemometrics 10, 325–342 (1996)

    Article  Google Scholar 

  5. Lukashin, V., Fuchs, R.: Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters. Bioinformatics 17, 405–414 (2001)

    Article  Google Scholar 

  6. Belacel, N., Hansen, P., Mladenovic, N.: Fuzzy J-Means: A new heuristic for fuzzy clustering. Pattern Recognition 35, 2193–2200 (2000)

    Article  MATH  Google Scholar 

  7. Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Transactions on Evolutionary Computation 3, 103–112 (1999)

    Article  Google Scholar 

  8. Al-Sultan, K.S., Fedjki, C.A.: A tabu search-based algorithm for the fuzzy clustering problem. Pattern Recognition 30, 2023–2030 (1997)

    Article  Google Scholar 

  9. Kao, Y., Lin, J.-C., Huang, S.-C.: Fuzzy Clustering by Differential Evolution. In: Eight International Conference on Intelligent System Designs and Applications (2008)

    Google Scholar 

  10. Maulik, U., Saha, I.: Automatic fuzzy clustering using modified Differential Evolution for Image Classification. IEEE transactions on Geoscience and Remote sensing 48(9) (September 2010)

    Google Scholar 

  11. Das, S., Konar, A.: Automatic Image Pixel Clustering with an Improved Differential Evolution. Applied Soft Computing, doi:10.1016/j.asoc.2007.12.008

    Google Scholar 

  12. Zhang, L., Ma, M., Liu, X., Sun, C., Liu, M., Zho, C.: Differential Evolution Fuzzy Clustering Algorithm based on Kernel methods. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 430–435. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. Rogalsky, T., Kocabiyik, S., Derksen, R.: Differential evolution in aerodynamic optimization. Canadian Aeronautics and Space Journal 46(4), 183–190 (2000)

    Google Scholar 

  15. Stumberger, G., Dolinar, D., Pahner, U., Hameyer, K.: Optimization of radial active magnetic bearings using the finite element technique and differential evolution algorithm. IEEE Transactions on Magnetics 36(4), 1009–1013 (2000)

    Article  Google Scholar 

  16. Doyle, S., Corcoran, D., Connell, J.: Automated mirror design using an evolution strategy. Optical Engineering 38(2), 323–333 (1999)

    Article  Google Scholar 

  17. Wang, F.S., Sheu, J.W.: Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast. Chemical Engineering Science 55(18), 3685–3695 (2000)

    Article  Google Scholar 

  18. Lampinen, J., Zelinka, I.: Mechanical Engineering Design Optimization by Differential Evolution. In: New Ideas in Optimization, pp. 127–146. McGraw-Hill, New York (1999)

    Google Scholar 

  19. Masters, T., Land, W.: A new training algorithm for the general regression neural network. In: IEEE International Conference on Systems, Man and Cybernetics, Computational Cybernetics and Simulation, vol. 3, pp. 1990–1994 (1997)

    Google Scholar 

  20. Zelinka, I., Lampinen, J.: An evolutionary learning algorithms for neural networks. In: 5th International Conference on Soft Computing MENDEL 1999, pp. 410–414 (1999)

    Google Scholar 

  21. Fischer, M.M., Hlavackova-Schindler, K., Reismann, M.: A global search procedure for parameter estimation in neural spatial interaction modelling. Regional Science 78(2), 119–134 (1999)

    Article  Google Scholar 

  22. Gang, L., Yiqing, T., Fu, T.: A fast evolutionary algorithm for neural network training using differential evolution. In: ICYCS 1999 Fifth International Conference for Young Computer Scientists, vol. 1, pp. 507–511 (1999)

    Google Scholar 

  23. Schmitz, G.P., Aldrich, C.: Combinatorial Evolution of Regression Nodes in Feedforward Neural Networks. Neural Networks 12(1), 175–189 (1999)

    Article  Google Scholar 

  24. Ilonen, J., Kamarainen, J.-K., Lampinen, J.: Differential Evolution Training Algorithm for Feedforward Neural Networks. Neural Processing Letters 17, 93–105 (2003)

    Article  Google Scholar 

  25. Bhat, T.R., Venkataramani, D., Ravi, V., Murty, C.V.S.: Improved differential evolution method for efficient parameter estimation in biofilter modeling. Biochemical Engineering Journal 28, 167–176 (2006)

    Article  Google Scholar 

  26. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-Adapting Control Prameters in Differential Evolution: A Comparative Study on numerical benchmark problems. IEEE transactions on Evolutionary Computation 10(6), 646–657 (2006)

    Article  Google Scholar 

  27. Zhang, J., Sanderson Arthur, C.: JADE: Adaptive Differential Evolution with Optional External Archive. IEEE transactions on Evolutionary Computation 13(5), 945–958 (2009)

    Article  Google Scholar 

  28. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm with strategy adaptation for Global Numerical Optimization. IEEE transactions on Evolutionary Computation 13(2) (April 2009)

    Google Scholar 

  29. Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 841–847

    Google Scholar 

  30. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine (1998), http://www.ics.uci.edu/mlearn/MLRepository.html

    Google Scholar 

  31. Forina, M., Armanino, C.: Eigenvector projection and simplified nonlinear mapping of fatty acid content of Italian olive oils. Annali di Chimica 72, 127–141 (1982), ftp://ftp.clarkson.edu/pub/hopkepk/Chemdata/Original/oliveoil.dat

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500 057, A P, India

    V. Ravi, Nupur Aggarwal & Nikunj Chauhan

Authors
  1. V. Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nupur Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikunj Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ravi, V., Aggarwal, N., Chauhan, N. (2010). Differential Evolution Based Fuzzy Clustering. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17563-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation