Data Clustering Based on an Efficient Hybrid of K-Harmonic Means, PSO and GA

  • Malihe Danesh
  • Mahmoud Naghibzadeh
  • Mohammad Reza Akbarzadeh Totonchi
  • Mohaddeseh Danesh
  • Behrouz Minaei
  • Hossein Shirgahi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6660)


Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.


Data clustering PSO KHM Genetic algorithm 


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  1. 1.
    Hu, G., Zhou, S., Guan, J., Hu, X.: Towards effective document clustering: A constrained K-means based approach. Information Processing & Management 44, 1397–1409 (2008)CrossRefGoogle Scholar
  2. 2.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to data mining, pp. 487–559. Addison-Wesley, Boston (2005)Google Scholar
  3. 3.
    Tjhi, W.C., Chen, L.H.: A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data. Fuzzy Sets and Systems 159, 371–389 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Zhou, H., Liu, Y.H.: Accurate integration of multi-view range images using k-means clustering. Pattern Recognition 41, 152–175 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Jain, A.K., Murty, M.N., Flynn, P.j.: Data clustering: A review. ACM Computational Survey 31, 264–323 (1999)CrossRefGoogle Scholar
  6. 6.
    Cui, X., Potok, T.E., Palathingal, P.: Document clustering using Particle Swarm Optimization. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, pp. 185–191 (2005)Google Scholar
  7. 7.
    Zhang, B., Hsu, M., Dayal, U.: K-harmonic means – a data clustering algorithm. Technical Report HPL-1999-124, Hewlett-Packard Laboratories (1999) Google Scholar
  8. 8.
    Hammerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proceedings of the 11th International Conference on Information and Knowledge Management, Virginia, USA, pp. 600–607 (2002)Google Scholar
  9. 9.
    Güngör, Z., Ünler, A.: K-harmonic means data clustering with simulated annealing heuristic. Applied Mathematics and Computation, 199–209 (2007)Google Scholar
  10. 10.
    Güngör, Z., Ünler, A.: K-harmonic means data clustering with tabu-search method. Applied Mathematical Modelling 32, 1115–1125 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Yang, F., Sun, T., Zhang, C.: An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Systems with Applications: An International Journal 36, 9847–9852 (2009)CrossRefGoogle Scholar
  12. 12.
    Jiang, H., Yi, S., Li, J., Yang, F., Hu, X.: Ant clustering algorithm with k-harmonic means clustering. Expert Systems with Applications 37, 8679–8684 (2010)CrossRefGoogle Scholar
  13. 13.
    Chu, S., Roddick, J.: A clustering algorithm using Tabu search approach with simulated annealing for vector quantization. Chinese Journal of Electronics 12, 349–353 (2003)Google Scholar
  14. 14.
    Huang, C.H., Pan, J.S., Lu, Z.H., Sun, S.H., Hang, H.M.: Vector quantization based on genetic simulated annealing. Signal Processing 81, 1513–1523 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Xu, R.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16, 645–678 (2005)CrossRefGoogle Scholar
  16. 16.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, New Jersey (1985)Google Scholar
  17. 17.
    Dalli, A.: Adaptation of the F-measure to cluster-based Lexicon quality evaluation. In: EACL (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Malihe Danesh
    • 1
  • Mahmoud Naghibzadeh
    • 1
  • Mohammad Reza Akbarzadeh Totonchi
    • 1
  • Mohaddeseh Danesh
    • 2
  • Behrouz Minaei
    • 2
  • Hossein Shirgahi
    • 3
  1. 1.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran
  2. 2.Department of Computer EngineeringIran University of Science and TechnologyTehranIran
  3. 3.Islamic Azad UniversityJouybarIran

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