Advertisement

An Improved Hybrid Genetic Clustering Algorithm

  • Yongguo Liu
  • Jun Peng
  • Kefei Chen
  • Yi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)

Abstract

In this paper, a new genetic clustering algorithm called IHGA-clustering is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGA-clustering, DHB operation is developed to improve the individual and accelerate the convergence speed, and partition-mergence mutation operation is designed to reassign objects among different clusters. Equipped with these two components, IHGA-clustering can stably output the proper result. Its superiority over HGA-clustering, GKA, and KGA-clustering is extensively demonstrated for experimental data sets.

Keywords

Genetic Algorithm Time Complexity Convergence Speed Cluster Problem Mutation Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brucker, P.: On the complexity of clustering problems. Lecture Notes in Economics and Mathematical Systems 157, 45–54 (1978)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, New Jersey (1988)MATHGoogle Scholar
  3. 3.
    Spath, H.: Cluster analysis algorithms. Wiley, Chichester (1980)MATHGoogle Scholar
  4. 4.
    Selim, S.Z., Ismail, M.A.: K-means-type algorithm: generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6, 81–87 (1984)CrossRefMATHGoogle Scholar
  5. 5.
    Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recognit. Lett. 17, 825–832 (1996)CrossRefGoogle Scholar
  6. 6.
    Babu, G.P., Murthy, M.N.: Clustering with evolutionary strategies. Pattern Recognit. 27, 321–329 (1994)CrossRefGoogle Scholar
  7. 7.
    Babu, G.P.: Connectionist and evolutionary approaches for pattern clustering. PhD dissertation. Indian Institute of Science, India (1994)Google Scholar
  8. 8.
    Al-sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recognit. 28, 1443–1451 (1995)CrossRefGoogle Scholar
  9. 9.
    Sung, C.S., Jin, H.W.: A tabu-search-based heuristic for clustering. Pattern Recognit. 33, 849–858 (2000)CrossRefGoogle Scholar
  10. 10.
    Selim, S.Z., Al-Sultan, K.S.: A simulated annealing algorithm for the clustering problem. Pattern Recognit. 24, 1003–1008 (1991)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bandyopadhyay, S., Maulik, U., Pakhira, M.K.: Clustering using simulated annealing with probabilisitc redistribution. Int. J. Pattern Recognit. Artif. Intell. 15, 269–285 (2001)CrossRefGoogle Scholar
  12. 12.
    Hall, L.O., Ozyurt, B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3, 103–112 (1999)CrossRefGoogle Scholar
  13. 13.
    Liu, Y.G., Chen, K.F., Li, X.M.: A hybrid genetic based clustering algorithm. In: Proceeding of The Third International Conference on Machine Learning and Cybernetics, Shanghai, pp. 1677–1682 (2004)Google Scholar
  14. 14.
    Fränti, P., Kivijärvi, J., Kaukoranta, T., Nevalainen, O.: Genetic algorithm for large-scale clustering problems. Comput. J. 40, 547–554 (1997)CrossRefGoogle Scholar
  15. 15.
    Krishna, K., Murty, M.N.: Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 29, 433–439 (1999)CrossRefGoogle Scholar
  16. 16.
    Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognit. 33, 1455–1465 (2000)CrossRefGoogle Scholar
  17. 17.
    Estivill-Castro, V.: Hybrid genetic algorithms are better for spatial clustering. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886, pp. 424–434. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf. Sci. 146, 221–237 (2002)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Kivijärvi, J., Fränti, P., Nevalainen, O.: Self-adaptive genetic algorithm for clustering. J. Heuristics 9, 113–129 (2003)CrossRefMATHGoogle Scholar
  20. 20.
    Wu, F.-X., Zhang, W.J., Kusalik, A.J.: A genetic K-means clustering algorithm applied to gene expression data. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS, vol. 2671, pp. 520–526. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Sheng, W.G., Tucker, A., Liu, X.H.: Clustering with niching genetic K-means algorithm. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 162–173. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Zhang, Q.W., Boyle, R.D.: A new clustering algorithm with multiple runs of iterative procedures. Pattern Recognit. 24, 835–848 (1991)CrossRefGoogle Scholar
  23. 23.
    Kim, D.J., Park, Y.W., Park, D.J.: A novel validity index for determination of the optimal number of clusters. IEICE Trans. Inf. Syst. E84-D, 281–285 (2001)Google Scholar
  24. 24.
    Chien, Y.T.: Interactive Pattern Recognition. Marcel Dekker, New York (1978)Google Scholar
  25. 25.
    Johnson, R.A., Wichern, D.W.: Applied multivariate statistical analysis. Prentice-Hall, New Jersey (1982)MATHGoogle Scholar
  26. 26.
    Fisher, R.A.: The use of multiple measurements in taxonomic problem. Annals of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yongguo Liu
    • 1
    • 2
    • 3
  • Jun Peng
    • 4
  • Kefei Chen
    • 3
  • Yi Zhang
    • 1
  1. 1.College of Computer Science and EngineeringUniversity of Electronic, Science and Technology of ChinaChengduP.R. China
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingP.R. China
  3. 3.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiP.R. China
  4. 4.School of Electronic Information EngineeringChongqing University of Science and TechnologyChongqingP.R. China

Personalised recommendations