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Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

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Foundations of Computational Intelligence Volume 4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 204))

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Abstract

Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation.

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References

  1. Alves, V.S., Campello, R.J.G.B., Hruschka, E.R.: A Fuzzy Variant of an Evolutionary Algorithm for Clustering. In: Proc. IEEE Int. Conference on Fuzzy Systems, pp. 375–380 (2007)

    Google Scholar 

  2. Arabie, L.J., Hubert, G., DeSoete, P.: Clustering and Classification. World Scientific, Singapore (1999)

    Google Scholar 

  3. Babu, G.P., Murty, M.N.: Clustering with Evolution Strategies. Pattern Recognition 27, 321–329 (1994)

    Article  Google Scholar 

  4. Babuška, R.: Fuzzy Modeling for Control. Kluwer, Dordrecht (1998)

    Google Scholar 

  5. Baldi, P., Brunak, S.: Bioinformatics - The Machine Learning Approach, 2nd edn. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  6. Bertone, P., Gerstein, M.: Integrative Data Mining: The New Direction in Bioinformatics – Machine Learning for Analyzing Genome-Wide Expression Profiles. IEEE Engineering in Medicine and Biology 20, 33–40 (2001)

    Article  Google Scholar 

  7. Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Trans. on Systems, Man and Cybernetics − B 28, 301–315 (1998)

    Article  Google Scholar 

  8. Bezdek, J.C., Hathaway, R.J.: Optimization of Fuzzy Clustering Criteria using Genetic Algorithms. In: Proc. IEEE World Congress on Computational Intelligence, pp. 589–594 (1994)

    Google Scholar 

  9. Bezdek, J.C., Hathaway, R.J., Howard, R.E., Wilson, C.A., Windham, M.P.: Local Convergence Analysis of a Grouped Variable Version of Coordinate Descent. Journal of Optimization Theory and Applications 54, 471–477 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  10. Bezdek, J.C., Hathaway, R.J., Sabin, M.J., Tucker, H.T.: Convergence Theory for Fuzzy C-Means: Counterexamples and Repairs. IEEE Trans. on Systems, Man and Cybernetics SMC-17, 873–877 (1987)

    Google Scholar 

  11. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press (1981)

    Google Scholar 

  12. Bigus, J.P.: Data Mining with Neural Networks. McGraw-Hill, New York (1996)

    Google Scholar 

  13. Campello, R.J.G.B., Alves, V.S., Hruschka, E.R.: On the Efficiency of Evolutionary Fuzzy Clustering. Journal of Heuristics, doi:10.1007/s10732-007-9059-6

    Google Scholar 

  14. Campello, R.J.G.B., Hruschka, E.R.: A Fuzzy Extension of the Silhouette Width Criterion for Cluster Analysis. Fuzzy Sets and Systems 157(21), 2858–2875 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  15. Davis, L.: Handbook of Genetic Algorithms. International Thomson Computer Press (1996)

    Google Scholar 

  16. de Oliveira, J.V., Pedrycz, W.: Advances in Fuzzy Clustering and its Applications. Wiley, Chichester (2007)

    Book  Google Scholar 

  17. Egan, M.A., Krishnamoorthy, M., Rajan, K.: Comparative Study of a Genetic Fuzzy C-Means Algorithm and a Validity Guided Fuzzy C-Means Algorithm for Locating Clusters in Noisy Data. In: Proc. IEEE World Congress on Computational Intelligence, pp. 440–445 (1998)

    Google Scholar 

  18. Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. Arnold Publishers (2001)

    Google Scholar 

  19. Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  20. Fazendeiro, P., Valente de Oliveira, J.: A Semantic Driven Evolutive Fuzzy Clustering Algorithm. In: Proc. IEEE Int. Conference on Fuzzy Systems, pp. 1–6 (2007)

    Google Scholar 

  21. Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, New York (1995)

    Google Scholar 

  22. Fogel, D.B., Simpson, P.K.: Evolving Fuzzy Clusters. In: Proc. IEEE Int. Conference on Neural Networks, pp. 1829–1834 (1993)

    Google Scholar 

  23. Fralley, C., Raftery, A.E.: How Many Clusters? Which Clustering Method? Answer via Model-Based Cluster Analysis. The Computer Journal 41, 578–588 (1998)

    Article  Google Scholar 

  24. Freitas, A.: A Review of Evolutionary Algorithms for Data Mining. In: Maimon, O., Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining, pp. 61–93. Springer, Heidelberg (2007)

    Google Scholar 

  25. Ghozeil, A., Fogel, D.B.: Discovering Patterns in Spatial Data using Evolutionary Programming. In: Proc. 1st Annual Conference on Genetic Programming, pp. 521–527 (1996)

    Google Scholar 

  26. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, 107–145 (2001)

    Article  MATH  Google Scholar 

  27. Hall, L.O., Bezdek, J.C., Boggavarpu, S., Bensaid, A.: Genetic Fuzzy Clustering. In: Proc. Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 411–415 (1994)

    Google Scholar 

  28. Hall, L.O., Özyurt, B.: Scaling Genetically Guided Fuzzy Clustering. In: Proc. Int. Symposium on Uncertainty Modeling and Analysis & Annual Conference of the North American Fuzzy Information Processing Society (ISUMA-NAFIPS), pp. 328–332 (1995)

    Google Scholar 

  29. Hall, L.O., Özyurt, I.B., Bezdek, J.C.: Clustering with a Genetically Optimized Approach. IEEE Trans. on Evolutionary Computation 3, 103–112 (1999)

    Article  Google Scholar 

  30. Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis: Methods for Classification. In: Data Analysis and Image Recognition. Wiley, Chichester (1999)

    Google Scholar 

  31. Hruschka, E.R., Campello, R.J.G.B., de Castro, L.N.: Clustering Gene-Expression Data: A Hybrid Approach that Iterates between k-Means and Evolutionary Search. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms, pp. 313–335. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  32. Hruschka, E.R., Campello, R.J.G.B., de Castro, L.N.: Evolutionary Search for Optimal Fuzzy C-Means Clustering. In: Proc. Int. Conference on Fuzzy Systems, pp. 685–690 (2004)

    Google Scholar 

  33. Hruschka, E.R., Campello, R.J.G.B., de Castro, L.N.: Evolving Clusters in Gene-Expression Data. Information Sciences 176, 1898–1927 (2006)

    Article  MathSciNet  Google Scholar 

  34. Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., Carvalho, A.C.P.L.F.: A Survey of Evolutionary Algorithms for Clustering. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews (to appear)

    Google Scholar 

  35. Hruschka, E.R., de Castro, L.N., Campello, R.J.G.B.: Evolutionary Algorithms for Clustering Gene-Expression Data. In: Proc. 4th IEEE Int. Conference on Data Mining, pp. 403–406 (2004)

    Google Scholar 

  36. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  37. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31, 264–323 (1999)

    Article  Google Scholar 

  38. Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Trans. on Knowledge and Data Engineering 16, 1370–1386 (2004)

    Article  Google Scholar 

  39. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data – An Introduction to Cluster Analysis. Wiley Series in Probability and Mathematical Statistics (1990)

    Google Scholar 

  40. Klawonn, F.: Fuzzy Clustering with Evolutionary Algorithms. In: Proc. of 7th Int. Fuzzy Systems Association (IFSA) World Congress, pp. 312–323 (1997)

    Google Scholar 

  41. Kolen, J.F., Hutcheson, T.: Reducing the Time Complexity of the Fuzzy C-Means Algorithm. IEEE Trans. on Fuzzy Systems 10, 263–267 (2002)

    Article  Google Scholar 

  42. Liu, H., Li, J., Chapman, M.A.: Automated Road Extraction from Satellite Imagery using Hybrid Genetic Algorithms and Cluster Analysis. Journal of Environmental Informatics 1(2), 40–47 (2003)

    Article  Google Scholar 

  43. Liu, J., Xie, W.: A Genetics-Based Approach to Fuzzy Clustering. In: Proc. Int. Conference on Fuzzy Systems, pp. 2233–2240 (1995)

    Google Scholar 

  44. MacQueen, J.B.: Some Methods of Classification and Analysis of Multivariate Observations. In: Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  45. Maulik, U., Bandyopadhyay, S.: Fuzzy Partitioning Using Real Coded Variable Length Genetic Algorithm for Pixel Classification. IEEE Trans. on Geosciences and Remote Sensing 41(5), 1075–1081 (2003)

    Article  Google Scholar 

  46. Mecca, G., Raunich, S., Pappalardo, A.: A New Algorithm for Clustering Search Results. Data and Knowledge Engineering 62, 504–522 (2007)

    Article  Google Scholar 

  47. Milligan, G.: A Monte Carlo Study of Thirty Internal Criterion Measures for Cluster Analysis. Psychometrika 46(2), 187–199 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  48. Milligan, G.W., Cooper, M.C.: An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika 50, 159–179 (1985)

    Article  Google Scholar 

  49. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A Study of some Fuzzy Cluster Validity Indices, Genetic Clustering and Application to Pixel Classification. Fuzzy Sets and Systems 155, 191–214 (2005)

    Article  MathSciNet  Google Scholar 

  50. Pal, N.R., Bezdek, J.C.: On Cluster Validity for the Fuzzy c-Means Model. IEEE Transactions on Fuzzy Systems 3(3) (1995)

    Google Scholar 

  51. Pantel, P.A.: Clustering by Commitee, PhD Thesis, Department of Computer Sciences of the University of Alberta, Canada (2003)

    Google Scholar 

  52. Park, H.-S., Yoo, S.-H., Cho, S.-B.: Evolutionary Fuzzy Clustering Algorithm with Knowledge-Based Evaluation and Applications for Gene Expression Profiling. Journal of Computational and Theoretical Nanoscience 2, 1–10 (2005)

    Article  Google Scholar 

  53. Rayward-Smith, V.J.: Metaheuristics for Clustering in KDD. In: Proc. IEEE Congress on Evolutionary Computation, pp. 2380–2387 (2005)

    Google Scholar 

  54. Rezaee, M.R., Lelieveldt, B.P.F., Reiber, J.H.C.: A New Cluster Validity Index for the Fuzzy c-Mean. Pattern Recognition Letters 19, 237–246 (1998)

    Article  MATH  Google Scholar 

  55. Srikanth, R., George, R., Warsi, N., Prabhu, D., Petry, F.E., Buckles, B.P.: A Variable-Length Genetic Algorithm for Clustering and Classification. Pattern Recognition Letters 16, 789–800 (1995)

    Article  Google Scholar 

  56. Sun, H., Wang, S., Jiang, Q.: FCM-Based Model Selection Algorithms for Determining the Number of Clusters. Pattern Recognition Letters 37, 2027–2037 (2004)

    MATH  Google Scholar 

  57. Valafar, F.: Pattern Recognition Techniques in Microarray Data Analysis: A Survey. Annals of New York Academy of Sciences 980, 41–64 (2002)

    Article  Google Scholar 

  58. Van Le, T.: Evolutionary Fuzzy Clustering. In: Proc. IEEE Congress on Evolutionary Computation, pp. 753–758 (1995)

    Google Scholar 

  59. Xu, R., Wunsch II, D.: Survey of Clustering Algorithms. IEEE Trans. on Neural Networks 16, 645–678 (2005)

    Article  Google Scholar 

  60. Yuan, B., Klir, G.J., Swan-Stone, J.F.: Evolutionary Fuzzy C-Means Clustering Algorithm. In: Proc. Int. Conference on Fuzzy Systems, pp. 2221–2226 (1995)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Sciences of the University of São Paulo at São Carlos (ICMC/USP), São Carlos, SP, Brazil

    D. Horta, M. Naldi, R. J. G. B. Campello, E. R. Hruschka & A. C. P. L. F. de Carvalho

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  1. D. Horta
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  2. M. Naldi
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  3. R. J. G. B. Campello
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  4. E. R. Hruschka
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  5. A. C. P. L. F. de Carvalho
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Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

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Horta, D., Naldi, M., Campello, R.J.G.B., Hruschka, E.R., de Carvalho, A.C.P.L.F. (2009). Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-01088-0_8

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