Skip to main content

Advertisement

SpringerLink
Log in

A two-leveled symbiotic evolutionary algorithm for clustering problems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abraham A, Jain L, Goldberg R (2005) Evolutionary multiobjective optimization. Springer, Berlin

    Book  MATH  Google Scholar 

  2. Al-Harbi SH, Rayward-Smith VJ (2006) Adaptive k-means for supervised clustering. Artif Intell 24:219–226

    Google Scholar 

  3. Al-Sultan K (1995) A tabu search approach to the clustering problem. Pattern Recognit 28(9):1443–1451

    Article  Google Scholar 

  4. Bäck T, Hammel U, Schwefel HP (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1(1):3–17

    Article  Google Scholar 

  5. Bandyopadhyay S, Maulik U (2002a) An evolutionary technique based on K-means algorithm for optimal clustering in ℝN. Inf Sci 146:221–237

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  7. Brown D, Huntley C (1992) A practical application of simulated annealing to clustering. Pattern Recognit 25(4):401–412

    Article  Google Scholar 

  8. Cowgill MC, Harvey RJ, Watson LT (1999) A genetic algorithm approach to cluster analysis. Comput Math Appl 37:99–108

    Article  MathSciNet  MATH  Google Scholar 

  9. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Recognit Mach Intell 1(2):224–227

    Article  Google Scholar 

  10. Everitt B, Landau S, Leese M (2001) Cluster analysis. Arnold, Sevenoaks

    MATH  Google Scholar 

  11. Fogel DB (2006) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, New York

    Google Scholar 

  12. Garai G, Chaudhuri BB (2004) A novel genetic algorithm for automatic clustering. Pattern Recognit Lett 25:173–187

    Article  Google Scholar 

  13. Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York

    Google Scholar 

  14. Gen M, Cheng R (2000) Genetic algorithm and engineering optimization, Wiley series in engineering design and automation. Wiley, New York

    Google Scholar 

  15. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading

    MATH  Google Scholar 

  16. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inform Syst 17:107–145

    Article  MATH  Google Scholar 

  17. Geva A (1999) Hierarchical unsupervised fuzzy clustering. IEEE Trans Fuzzy Set 7(6):723–733

    Article  Google Scholar 

  18. Handl J, Meyer B (2007) Ant-based and swarm-based clustering. Swarm Intell 1(2):95–113

    Article  Google Scholar 

  19. Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42:228–234

    Article  Google Scholar 

  20. Hruschka ER, Campello RGB, Freitas AA, Carvalho APL (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C 39(2):133–155

    Article  Google Scholar 

  21. Hruschka ER, Ebecken NFF (2003) A genetic algorithm for cluster analysis. Intell Data Anal 7:15–25

    Google Scholar 

  22. Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  23. Jeong YS, Kim SJ, Jeong MK (2008) Automatic identification of defect patterns in semiconductor wafer maps using spatial correlogram and dynamic time warping. IEEE Trans Semicond Manuf 21(4):625–637

    Article  MathSciNet  Google Scholar 

  24. Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis. Prentice-Hall, New York

    Google Scholar 

  25. Kim JY, Kim Y, Kim YK (2001) An endosymbiotic evolutionary algorithm for optimization. Appl Intell 15(2):117–130

    Article  MATH  Google Scholar 

  26. Kim YK, Kim JY, Kim Y (2000) A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Appl Intell 13:247–258

    Article  Google Scholar 

  27. Kim YK, Kim JY, Shin KS (2007) An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling. J Intell Manuf 18:631–645

    Article  Google Scholar 

  28. Kim YK, Park KT, Ko JS (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171

    Article  MathSciNet  MATH  Google Scholar 

  29. Koza JR (1992) Genetic programming. MIT Press, Cambridge

    MATH  Google Scholar 

  30. Korkmaz EE (2010) Multi-objective Genetic Algorithms for grouping problems. Appl Intell 33:179–192

    Article  Google Scholar 

  31. Kuncheva LI, Bezdek JC (1997) Selection of cluster prototypes from data by a genetic algorithm. In: Proceedings of 5th European congress on intelligent techniques and soft computing, pp 1683–1688

    Google Scholar 

  32. Lu Y, Lu S, Fotouhi F, Deng Y, Brown SJ (2004) FGKA: a fast genetic K-means clustering algorithm. In: Proceedings of ACM symposium on applied computing, pp 622–623

    Chapter  Google Scholar 

  33. Margulis L (1970) Origin of eukaryotic cells. Yale University Press, New Haven

    Google Scholar 

  34. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33:1455–1465

    Article  Google Scholar 

  35. Michalewics Z (1996) Genetic algorithms+data structures=evolution programs, 3rd edn. Springer, Berlin

    Google Scholar 

  36. Moriarty DE, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evol Comput 5:373–399

    Article  Google Scholar 

  37. Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit Lett 17:825–832

    Article  Google Scholar 

  38. Ozyer T, Zhang M, Alhajj R (2009) Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Appl Intell 31:318–331

    Article  Google Scholar 

  39. Ozyer T, Zhang M, Alhajj R (2011) Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl Intell 35:110–122

    Article  Google Scholar 

  40. Potter MA (1997) The design and analysis of a computational model of cooperative coevolution. PhD dissertation, George Mason University

  41. Rosin CD, Belew RK (1997) New methods for competitive coevolution. Evol Comput 5:1–29

    Article  Google Scholar 

  42. Saitta S, Raphael B, Smith IFC (2008) A comprehensive validity index for clustering. Intell Data Anal 12:529–548

    Google Scholar 

  43. Selim S, Alsultan K (1991) A simulated annealing algorithm for the clustering problems. Pattern Recognit 24(10):1003–1008

    Article  MathSciNet  Google Scholar 

  44. Sung C, Jin H (2000) A Tabu-search-based heuristic for clustering. Pattern Recognit 33:849–858

    Article  Google Scholar 

  45. Syswerda G (1991) A study of reproduction in generational and steady-state genetic algorithms. In: Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 94–101

    Google Scholar 

  46. Tou JT, Gonzalez RC (1974) Pattern recognition principles. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  47. Tseng LY, Yang SB (2001) A genetic approach to the automatic clustering problem. Pattern Recognit 34:415–424

    Article  MATH  Google Scholar 

  48. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3):586-600

    Google Scholar 

  49. Wang CH (2008) Recognition of semiconductor defect patterns using spatial filtering and spectral clustering. Expert Syst Appl 34:1914–1923

    Article  Google Scholar 

  50. Whitley D (1989) The genitor algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In: Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Mateo, pp 116–121

    Google Scholar 

  51. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  52. Yuan T, Kuo W (2008) Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference. Eur J Oper Res 190:228–240

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rutgers Center for Operations Research (RUTCOR), Rutgers, The State University of New Jersey, Piscataway, USA

    Kyoung Seok Shin & Myong K. Jeong

  2. Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, USA

    Young-Seon Jeong & Myong K. Jeong

Authors
  1. Kyoung Seok Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young-Seon Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Myong K. Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Young-Seon Jeong or Myong K. Jeong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shin, K.S., Jeong, YS. & Jeong, M.K. A two-leveled symbiotic evolutionary algorithm for clustering problems. Appl Intell 36, 788–799 (2012). https://doi.org/10.1007/s10489-011-0295-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-011-0295-y

Keywords

Search

Navigation