A New Approach for Partitional Clustering Using Entropy Notation and Hopfield Network

  • Vahid Abrishami
  • Maryam Sabzevari
  • Mahdi Yaghobi
Conference paper


This paper proposes a new clustering algorithm which employs an improved stochastic competitive Hopfield network in order to organize data patterns into natural groups, or clusters, in an unsupervised manner. This Hopfield network uses an entropy based energy function to overcome the problem of insufficient understanding of the data and to obtain the optimal parameters for clustering. Additionally, a chaotic variable is introduced in order to escape from the local minima and gain a better clustering. By minimizing the entropy of each cluster using Hopfield network, we achieve a superior accuracy to that of the best existing algorithms such as optimal competitive Hopfield model, stochastic optimal competitive Hopfield network, k-means and genetic algorithm. The experimental results demonstrate the scalability and robustness of our algorithm over large datasets.


Data Item Cluster Problem Quadratic Assignment Problem Partitional Cluster Maximum Clique 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967).Google Scholar
  2. 2.
    Bezdek, J. C., Ehrlich, R., & Full, W.: Fcm: The fuzzy c-means clustering algorithm. J. Computers & Geosciences, 10 (2-3), pp. 191–203 (1984).CrossRefGoogle Scholar
  3. 3.
    Sarafis, I., Zalzala, A. M., Trinder, P. W.: A genetic rule-based data clustering toolkit. In: Proceedings of the Evolutionary Computation (CEC '02), pp. 1238-1243. IEEE Computer Society, Washington, DC (2002).Google Scholar
  4. 4.
    Barbara, D., Couto, J., Li, Y.: COOLCAT An entropy based algorithm for categorical clustering. In: Proceedings of the eleventh international conference on Information and knowledge management. ACM Press (2002).Google Scholar
  5. 5.
    Galán-Marín, G., Mérida-Casermeiro, E., and Muñoz-Pérez.: Modeling competitive Hopfield networks for the maximum clique problem. J. Comput. Oper. Res. 30, pp. 603–624 (2003).MATHCrossRefGoogle Scholar
  6. 6.
    Wang, J. and Zhou, Y.: Stochastic optimal competitive Hopfield network for partitional clustering. J. Expert Syst. Appl. 36, pp. 2072–2080 (2009).CrossRefGoogle Scholar
  7. 7.
    Azamimi, A., Uwate, Y., Nishio, Y.: An Improvement in Pattern Recognition Problem Using Chaotic BP Learning Algorithm. In: Proceedings of RISP International Workshop on Nonlinear Circuits and Signal Processing, pp. 213–216 (2009).Google Scholar
  8. 8.
    Krink, T., Paterlini, S.: Differential Evolution and Particle Swarm Optimization in Partitional Clustering. J. Computational Statistics and Data Analysis. 50, pp. 1220–1247 (2006).CrossRefMathSciNetGoogle Scholar
  9. 9.
    Jarboui, B., Cheikh, M., Siarry, P., & Rebai, A.: Combinatorial particle swarm optimization (CPSO) for partitional clustering problem. J. Applied Mathematics and Computation. 192(2), pp. 337–345 (2007).MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository []. Irvine, CA: University of California, School of Information and Computer Science.

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Young Researchers Club (YRC), Islamic Azad UniversityMashhad BranchIran
  2. 2.Islamic Azad UniversityMashhad BranchIran

Personalised recommendations