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Foraging Agent Swarm Optimization with Applications in Data Clustering

  • Kevin M. Barresi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)

Abstract

This paper proposes a novel method of swarm optimization called Foraging Agent Swarm Optimization (FASO). FASO is designed to converge on multiple optima in both gradient and point-based search spaces. FASO also operates well in situations where “field optima” are desired, rather than single-point optima. The utility and effectiveness of FASO in a non-gradient search space is demonstrated in the context of data clustering, where we present Foraging Agent Swarm Clustering (FASC). FASC provides several benefits over conventional clustering, such as the ability to automatically determine the number of clusters, and strong performance in both noisy and sparse data sets. FASC is demonstrated to outperform existing methods of clustering in a variety of situations. Positive results by FASC in data clustering suggest that FASO has a promising future in other optimization applications as well.

Keywords

Particle Swarm Optimization Globular Cluster Data Cluster Sensor Range Neighboring Agent 
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.

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References

  1. 1.
    Alam, S., Dobbie, G., Riddle, P.: An evolutionary particle swarm optimization algorithm for data clustering. In: IEEE Swarm Intelligence Symposium, SIS 2008, pp. 1–6 (September 2008)Google Scholar
  2. 2.
    Aljarah, I., Ludwig, S.: A new clustering approach based on glowworm swarm optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2642–2649 (June 2013)Google Scholar
  3. 3.
    Cui, X., Potok, T., Palathingal, P.: Document clustering using particle swarm optimization. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 185–191 (June 2005)Google Scholar
  4. 4.
    Esmin, A., Coelho, R.: Consensus clustering based on particle swarm optimization algorithm. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2280–2285 (October 2013)Google Scholar
  5. 5.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231. AAAI Press (1996)Google Scholar
  6. 6.
    Fränti, P., Virmajoki, O.: Iterative shrinking method for clustering problems. Pattern Recogn. 39(5), 761–775 (2006)CrossRefzbMATHGoogle Scholar
  7. 7.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1) (March 2007)Google Scholar
  8. 8.
    Jain, A., Law, M.: Data clustering: A user’s dilemma. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 1–10. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Krishnanand, K.N., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 84–91 (June 2005)Google Scholar
  10. 10.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations (1967)Google Scholar
  11. 11.
    Niu, B., Duan, Q., Liang, J.: Hybrid bacterial foraging algorithm for data clustering. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 577–584. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Olesen, J., Cordero, H.J., Zeng, Y.: Auto-clustering using particle swarm optimization and bacterial foraging. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds.) ADMI 2009. LNCS, vol. 5680, pp. 69–83. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Szabo, A., de Castro, L., Delgado, M.: The proposal of a fuzzy clustering algorithm based on particle swarm. In: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 459–465 (October 2011)Google Scholar
  14. 14.
    Van Der Merwe, D.W., Engelbrecht, A.: Data clustering using particle swarm optimization. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 215–220 (December 2003)Google Scholar
  15. 15.
    Wan, M., Li, L., Xiao, J., Wang, C., Yang, Y.: Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems 38(2), 321–341 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kevin M. Barresi
    • 1
  1. 1.Department of Electrical and Computer EngineeringStevens Institute of TechnologyNew JerseyUSA

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