Abstract
The shortcoming of traditional spectral clustering algorithm is its dependence on initial value. This paper proposes a spectral clustering algorithm based on the particle swarm optimization, considering the characteristic of the good global and local optimization capability and the randomization of initial population. According to the example analysis, the spectral clustering algorithm based on the particle swarm optimization has overcome the shortcoming of excessive dependence on initial value of the traditional spectral clustering algorithm. The accuracy of the cluster is improved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, X., Dai, G., Yang, L.: Overview of spectral clustering algorithm. Comput. Sci. 35(07), 14–18 (2008)
Zhang, X., Qian, X.: Immune spectral clustering algorithm for image segmentation. J. Softw. 21(9), 2196–2205 (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Langdon, W.B., Poli, R.: Evolving problems to learn about particle swarm and other optimizers. Proc. CEC-2005. 1, 81–88 (2005)
Eberhart, R, Shi, Y.: Particle swarm optimization: Developments, applications and resources . Proc. IEEE Congr. Evol. Comput. 1(1), 81–86 (2001)
Eberhart, R., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)
Wang, W., Tang, Y.: Current situation and prospect of particle swarm optimization. J. Zhejiang Univ. Technol. 35(2), 136–141 (2007)
Wang, L., Bo, L., Jiao, L.: Density-sensitive semi-supervised spectral clustering. J. Softw. 18(10), 2412–2422 (2007)
Su, S., Wang, J., Fang, J.: Overview applications and research on particle swarm optimization algorithm. Comput. Technol. Dev. 17(5), 248–250 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this paper
Cite this paper
Wang, F. (2013). A Spectral Clustering Algorithm Based on Particle Swarm Optimization. In: Wong, W.E., Ma, T. (eds) Emerging Technologies for Information Systems, Computing, and Management. Lecture Notes in Electrical Engineering, vol 236. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7010-6_24
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7010-6_24
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7009-0
Online ISBN: 978-1-4614-7010-6
eBook Packages: EngineeringEngineering (R0)