Skip to main content
SpringerLink
Log in

Automatic Hard Clustering Using Improved Differential Evolution Algorithm

  • Chapter
Metaheuristic Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 178))

Abstract

This chapter describes a Differential Evolution (DE) based algorithm for the automatic clustering of large unlabeled datasets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of clusters in the data ‘on the run’. Superiority of the new method has been demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. An interesting practical application of the proposed method to automatic segmentation of images is also illustrated.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  2. Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Transactions on Systems Man and Cybernetics - Part A 38(1), 218–237 (2008)

    Article  Google Scholar 

  3. Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35, 1197–1208 (2002)

    Article  MATH  Google Scholar 

  4. Omran, M., Salman, A., Engelbrecht, A.: Dynamic clustering using particle swarm optimization with application in unsupervised image classification. In: Fifth World Enformatika Conference (ICCI 2005), Prague, Czech Republic (2005)

    Google Scholar 

  5. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  7. Chou, C.H., Su, M.C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Analysis and Applications 7(2), 205–220 (2004)

    Article  MathSciNet  Google Scholar 

  8. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)

    Article  Google Scholar 

  9. Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4, 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  10. Bezdek, J.C.: Numerical taxonomy with fuzzy sets. Journal of Math. Biol., 157–171 (1974)

    Google Scholar 

  11. Bezdek, J.C.: Cluster validity with fuzzy sets. Journal of Cybernetics (3), 58–72 (1974)

    Google Scholar 

  12. Xie, X., Beni, G.: Validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Machine Learning 3, 841–846 (1991)

    Article  Google Scholar 

  13. Gustafson, D., Kessel, W.: Fuzzy clustering with a fuzzy covariance matrix. In: Proc. IEEE CDC, San Diego, CA, USA, pp. 761–766 (1979)

    Google Scholar 

  14. Das, S., Konar, A., Chakraborty, U.K.: Two Improved differential evolution schemes for faster global search. In: ACM-SIGEVO Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2005), Washington DC (2005)

    Google Scholar 

  15. Day, W.H., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification 1, 1–24 (1984)

    Article  Google Scholar 

  16. Blake, C., Keough, E., Merz, C.J.: UCI repository of machine learning database (1998), http://www.ics.uci.edu/~mlearn/MLrepository.html

  17. Pal, S.K., Majumder, D.D.: Fuzzy sets and decision-making approaches in vowel and speaker recognition. IEEE Transactions on System, Man and Cybernertics SMC-7, 625–629 (1977)

    Article  MATH  Google Scholar 

  18. van den Bargh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computing 8(3) (June 2004)

    Google Scholar 

  19. Olson, C.: Parallel algorithms for hierarchical clustering. Parallel Computing 21(8), 1313–1325 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  20. Flury, B.: A First Course in Multivariate Statistics, vol. 28. Springer, Heidelberg (1997)

    MATH  Google Scholar 

  21. Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, Cybernetics 28, 301–315 (1998)

    Article  Google Scholar 

  22. Kothari, R., Pitts, D.: On finding the number of clusters. Pattern Recognition Letters 20, 405–416 (1999)

    Article  Google Scholar 

  23. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  24. Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, London (1974)

    MATH  Google Scholar 

  25. Trivedi, M.M., Bezdek, J.C.: Low-level segmentation of aerial images with fuzzy clustering. IEEE Trans. on Systems, Man and Cybernetics 16 (1986)

    Google Scholar 

  26. Ball, G., Hall, D.: A clustering technique for summarizing multivariate data. Behavioral Science 12, 153–155 (1967)

    Article  Google Scholar 

  27. Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer Journal 11(2), 185–194 (1968)

    MATH  Google Scholar 

  28. Omran, M., Engelbrecht, A., Salman, A.: Particle swarm optimization method for image clustering. International Journal of Pattern Recognition and Artificial Intelligence 19(3), 297–322 (2005)

    Article  Google Scholar 

  29. Das, S., Konar, A., Abraham, A.: Spatial information based image segmentation with a modified particle swarm optimization. In: Sixth International Conference on Intelligent System Design and Applications (ISDA) 2006, Jinan, Shangdong, China. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  30. Das, S., Abraham, A., Sarkar, S.K.: A hybrid rough-swarm algorithm for image pixel classification. In: Proceedings of 6th International Conference on Hybrid Intelligent Systems (HIS 2006), AUT Technology Park, Auckland, New Zealand. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  31. Das, S., Konar, A.: Automatic Image Pixel Clustering with an Improved Differential Evolution. Applied Soft Computing Journal 9(1), 226–236 (2009)

    Article  Google Scholar 

  32. Press, W.H., Teukolsky, S.l.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)

    Google Scholar 

Download references

Authors
  1. Swagatam Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Konar
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Das, S., Abraham, A., Konar, A. (2009). Automatic Hard Clustering Using Improved Differential Evolution Algorithm . In: Metaheuristic Clustering. Studies in Computational Intelligence, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93964-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-93964-1_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92172-1

  • Online ISBN: 978-3-540-93964-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation