Abstract
A hybrid clustering algorithm based on the artificial immune theory is presented in this paper. The method is inspired by the clone selection and memory principle. The problem of local optimal can be avoided by introducing the differentiation of memory antibody and inhibition mechanism. In addition, the K-means algorithm is used as a search operator in order to improve the convergence speed. The proposed algorithm can obtain the better data convergence compared with the K-means algorithm based clustering approach and artificial immune based approach. Simulate experimental results indicate the hybrid algorithm has a faster convergence speed and the obtained clustering centers can get strong stability.
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
Preview
Unable to display preview. Download preview PDF.
References
Xu, Y., Chen, C., et al.: An Improved Clustering Algorithm For k-means. J. Computer Application and Software 25, 275–277 (2008)
Park, H.S., Jun, C.H.: A Simple and Fast Algorithm for K-means Clustering. J. Expert Systems with Applications 36, 3336–3341 (2009)
Yang, X.: Research of Key Techniques in Cluster Analysis. D. Hangzhou Zhejiang University (2005)
Yang, F.: Adaptive Clone and Suppression Artificial Immune Algorithm. J. Application Research of Computers 28, 481–484 (2011)
Mo, H.: Principle and Application of Artificial Immune System. Harbin industrial university press, Harbin (2002)
Liu, T., Wang, Y., et al.: A Cluster Algorithm Based on Artificial Immune System. J. Computer Project and Design 25, 2051–2053 (2004)
Ding, Y., Ren, L.: Artificial Immune System. J. Theory and Application Pattern Recognition and Artificial Intelligence 13, 12–14 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Y., Hu, Z. (2011). Hybrid Clustering Algorithm Based on the Artificial Immune Principle. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-24728-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24727-9
Online ISBN: 978-3-642-24728-6
eBook Packages: Computer ScienceComputer Science (R0)