Data clustering using multivariant optimization algorithm

  • Qin-Hu Zhang
  • Bao-Lei Li
  • Ya-Jie Liu
  • Lian Gao
  • Lan-Juan Liu
  • Xin-Ling ShiEmail author
Original Article


Data clustering is one of the most popular techniques in data mining to group data with great similarity and high dissimilarity into each cluster. This paper presents a new clustering method based on a novel heuristic optimization algorithm proposed recently and named as multivariant optimization algorithm (MOA) to locate the optimal solution automatically through global and local alternating search implemented by a global exploration group and several local exploitation groups. In order to demonstrate the performance of MOA-clustering method, it is applied to group six real-life datasets to obtain their clustering results, which may be compared with those received by employing K-means algorithm, genetic algorithm and particle swarm optimization. The results show that the proposed clustering algorithm is an effective and feasible method to reach a high accurate rate and stability in clustering problems.


Data clustering Cluster centers Multivariant optimization algorithm Global and local optimization 


  1. 1.
    Evangelou IE, Hadjimitsis DG, Lazakidou AA, Clayton C (2001) Data mining and knowledge discovery in complex image data using artificial neural networks. Workshop on Complex Reasoning an Geographical Data, CyprusGoogle Scholar
  2. 2.
    Selim SZ, Ismail MA (1984) K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans Pattern Anal Mach Intell 6:81–87CrossRefzbMATHGoogle Scholar
  3. 3.
    Hitendra Sarma T, Viswanath P, Eswara Reddy B (2013) A hybrid approach to speed-up the k-means clustering method. Int J Mach Learn Cybernet 4(2):107–117CrossRefGoogle Scholar
  4. 4.
    Jinxin D, Minyong Qi (2009) A new algorithm for clustering based on particle swarm optimization and K-means. IEEE Int Conf Artif Intell Comput Intell 4:264–268Google Scholar
  5. 5.
    Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762CrossRefGoogle Scholar
  6. 6.
    Filho JLR, Treleaven PC, Alippi C (1994) Genetic algorithm programming environments. IEEE Comput 27:28–43CrossRefGoogle Scholar
  7. 7.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks (ICW). vol IV, Perth, Australia, pp 1942–1948Google Scholar
  8. 8.
    Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3–2:95–99CrossRefGoogle Scholar
  9. 9.
    Maulik U, Bandyopadhyay S (2000) Genetic algorithm based clustering technique. Pattern Recogn 33:1455–1465CrossRefGoogle Scholar
  10. 10.
    Chiou YC, Lan LW (2001) Theory and methodology genetic clustering algorithms. Eur J Oper Res 135:413–427MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Merwe VD, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation 2003 (CEC 2003), Canbella, Australia, pp 215–220Google Scholar
  12. 12.
    Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybernet 4(4):391–400CrossRefGoogle Scholar
  13. 13.
    Cui XH, Potok TE, Palathingal P (2005) Document clustering using particle swarm optimization. IEEE swarm intelligence symposium 2005. Pasadena, California, pp 185–191Google Scholar
  14. 14.
    Chen CY, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. International conference on networking, sensing control Taipei, Taiwan, March 21–23Google Scholar
  15. 15.
    Omran M, Engelbrecht AP, Salman A (2005) Particle swarm optimization method for image clustering. Int J Pattern Recogn Artif Intell 19(3):297–322CrossRefGoogle Scholar
  16. 16.
    Chuang LY, Hsiao CJ, Yang CH (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38(12):14555–14563CrossRefGoogle Scholar
  17. 17.
    Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings, evolutionary computationGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Qin-Hu Zhang
    • 1
  • Bao-Lei Li
    • 1
  • Ya-Jie Liu
    • 1
  • Lian Gao
    • 1
  • Lan-Juan Liu
    • 1
  • Xin-Ling Shi
    • 1
    Email author
  1. 1.Department of Electronic Engineering, School of InformationYunnan UniversityKunmingChina

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