Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 938239, 16 pages
http://dx.doi.org/10.1155/2014/938239
Research Article

A Hybrid Monkey Search Algorithm for Clustering Analysis

1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China
2Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning Guangxi 530006, China

Received 6 November 2013; Accepted 22 January 2014; Published 4 March 2014

Academic Editors: M. Lopez-Nores, D.-C. Lou, L. Martínez, D. Wu, and L. Xiao

Copyright © 2014 Xin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. B. Cattell, “The description of personality: basic traits resolved into clusters,” Journal of Abnormal and Social Psychology, vol. 38, no. 4, pp. 476–506, 1943. View at Publisher · View at Google Scholar · View at Scopus
  2. K. R. Zalik, “An efficient k′-means clustering algorithm,” Pattern Recognition Letters, vol. 29, no. 9, pp. 1385–1391, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Zhang, M. Hsu, and U. Dayal, “K-harmonic means-a data clustering algorithm,” Hewlett-Packard Labs Technical Report HPL-1999-124, 1999. View at Google Scholar
  4. P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, “An ant colony approach for clustering,” Analytica Chimica Acta, vol. 509, no. 2, pp. 187–195, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Kao and K. Cheng, An ACO-Based Clustering Algorithm, Springer, Berlin, Germany.
  6. E. H. L. Aarts and J. H. Korst, Simulated Annealing and Boltzmann Machines, John Wiley and Sons, New York, NY, USA, 1989.
  7. T. Niknam, J. Olamaei, and B. Amiri, “A hybrid evolutionary algorithm based on ACO and SA for cluster analysis,” Journal of Applied Sciences, vol. 8, no. 15, pp. 2695–2702, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Niknam, B. Bahmani Firouzi, and M. Nayeripour, “An efficient hybrid evolutionary algorithm for cluster analysis,” World Applied Sciences Journal, vol. 4, no. 2, pp. 300–307, 2008. View at Google Scholar
  9. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, IEEE Service Center, Piscataway, NJ, USA, December 1995. View at Scopus
  10. M. Omran, A. P. Engelbrecht, and A. Salman, “Particle swarm optimization method for image clustering,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 19, no. 3, pp. 297–321, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. V. D. Merwe and A. P. Engelbrecht, “Data clustering using particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '03), pp. 215–220, 2003.
  12. Y. T. Kao, E. Zahara, and I. W. Kao, “A hybridized approach to data clustering,” Expert Systems with Applications, vol. 34, no. 3, pp. 1754–1762, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Niknam, B. Amiri, J. Olamaei, and A. Arefi, “An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering,” Journal of Zhejiang University A, vol. 10, no. 4, pp. 512–519, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Niknam and B. Amiri, “An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis,” Applied Soft Computing Journal, vol. 10, no. 1, pp. 183–197, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University Press, Erciyes, Turkey, 2005. View at Google Scholar
  16. D. Karaboga and C. Ozturk, “A novel clustering approach: artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 652–657, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Zou, Y. Zhu, H. Chen, and X. Sui, “A clustering approach using cooperative artificial bee colony algorithm,” Discrete Dynamics in Nature and Society, vol. 2010, Article ID 459796, 16 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. K. E. Voges and N. K. L. Pope, “Rough clustering using an evolutionary algorithm,” in Proceedings of the 45th Hawaii International Conference on System Sciences (HICSS '12), pp. 1138–1145, January 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Ruiqing and T. Wansheng, “Monkey algorithm for global numerical optimization,” Journal of Uncertain Systems, vol. 2, no. 3, pp. 164–175, 2008. View at Google Scholar
  20. J.-R. Wang, Y. X. Yu, and Y. Zeng, “Discrete monkey algorithm and its application in transmission network expansion planning,” Journal of Tianjin University, vol. 43, no. 9, pp. 798–803, 2010. View at Google Scholar · View at Scopus
  21. T.-H. Yi, H.-N. Li, and X.-D. Zhang, “A modified monkey algorithm for optimal sensor placement in structural health monitoring,” Smart Materials and Structures, vol. 21, no. 10, pp. 65–69, 2012. View at Google Scholar
  22. J.-J. Zhang, Y.-P. Zhang, and J.-Z. Sun, “Intrusion detection technology based on monkey algorithm,” Computer Engineering, vol. 37, no. 14, pp. 131–133, 2011. View at Google Scholar
  23. Z. Tao, X. Yu, and Z. Mali, “Optimization of gas filling station project scheduling problem based on monkey algorithm,” in Value Engineering, pp. 90–92, 2010. View at Google Scholar
  24. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, 14, pp. 281–297, 1967.
  25. X. Chen and J. Zhang, “Clustering algorithm based on improved particle swarm optimization,” Journal of Computer Research and Development, pp. 287–291, 2012. View at Google Scholar
  26. X. Liu, Q. Sha, Y. Liu, and X. Duan, “Analysis of classification using particle swarm optimization,” Computer Engineering, vol. 32, no. 6, pp. 201–217, 2006. View at Google Scholar · View at Scopus
  27. J. Kiefer and J. Wolfowitz, “Stochastic estimation of the maximum of a regression function,” The Annals of Mathematical Statistics, vol. 23, no. 3, pp. 462–466, 1952. View at Google Scholar
  28. J. C. Spall, “An overview of the simultaneous perturbation method for efficient optimization,” Johns Hopkins APL Technical Digest, vol. 19, no. 4, pp. 482–492, 1998. View at Google Scholar · View at Scopus
  29. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University Press, Erciyes, Turkey, 2005. View at Google Scholar
  30. J. Kennedy and C. Eberhartr, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (ICNN '96), pp. 1942–1948, IEEE Piscataway, Perth, Australia, 1995.
  31. C. L. Blake and C. J. Merz, “UCI Repository of Machine Learning Databases,” http://archive.ics.uci.edu/ml/datasets.html.
  32. F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to participle swam optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004. View at Publisher · View at Google Scholar · View at Scopus