A Novel Clustering Technique Based on Improved Noising Method

  • Yongguo Liu
  • Wei Zhang
  • Dong Zheng
  • Kefei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this article, the clustering problem under the criterion of minimum sum of squares clustering is considered. It is known that this problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. To explore the proper result, a novel clustering technique based on improved noising method called INMC is developed, in which one-step DHB algorithm as the local improvement operation is integrated into the algorithm framework to fine-tune the clustering solution obtained in the process of iterations. Moreover, a new method for creating the neighboring solution of the noising method called mergence and partition operation is designed and analyzed in detail. Compared with two noising method based clustering algorithms recently reported, the proposed algorithm greatly improves the performance without the increase of the time complexity, which is extensively demonstrated for experimental data sets.


Time Complexity Tabu Search Cluster Result Cluster Technique Cluster Problem 
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.


  1. 1.
    Brucker, P.: On the complexity of clustering problems. Lecture Notes in Economics and Mathematical Systems 157, 45–54 (1978)MathSciNetGoogle Scholar
  2. 2.
    Spath, H.: Cluster analysis algorithms. Wiley, Chichester (1980)Google Scholar
  3. 3.
    Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, New Jersey (1988)zbMATHGoogle 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)zbMATHCrossRefGoogle 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.
    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
  10. 10.
    Liu, Y.G., Liu, Y., Chen, K.F.: Clustering with noising method. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 209–216. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Charon, I., Hudry, O.: The noising method: a new method for combinatorial optimization. Oper. Res. Lett. 14, 133–137 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Chelouah, R., Siarry, P.: Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur. J. Oper. Res. 148, 335–348 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Chelouah, R., Siarry, P.: A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions. Eur. J. Oper. Res. 161, 636–654 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Charon, I., Hudry, O.: The noising method: a generalization of some metaheuristics. Eur. J. Oper. Res. 135, 86–101 (2001)zbMATHCrossRefGoogle Scholar
  15. 15.
    Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. Wiley, New York (1972)Google Scholar
  16. 16.
    Ismail, M.A., Selim, S.Z., Arora, S.K.: Efficient clustering of multidimensional data. In: Proceedings of 1984 IEEE International Confference on System, Man, and Cybernetics, Halifax, pp. 120–123 (1984)Google Scholar
  17. 17.
    Ismail, M.A., Kamel, M.S.: Multidimensional data clustering utilizing hybrid search strategies. Pattern Recognit. 22, 75–89 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Zhang, Q.W., Boyle, R.D.: A new clustering algorithm with multiple runs of iterative procedures. Pattern Recognit. 24, 835–848 (1991)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Chien, Y.T.: Interactive Pattern Recognition. Marcel-Dekker, New York (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yongguo Liu
    • 1
    • 2
    • 4
  • Wei Zhang
    • 3
  • Dong Zheng
    • 4
  • Kefei Chen
    • 4
  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 and Modern Education TechnologyChongqing Education CollegeChongqingP. R. China
  4. 4.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiP. R. China

Personalised recommendations