Abstract
Immune Algorithms have been used widely and successfully in many computational intelligence areas including clustering. Given the large number of variants of each operator of this class of algorithms, this paper presents a study of the convergence properties of an improved artificial immune algorithm for clustering(DCAAIN algorithm), which has better clustering quality and higher data compression rate rather than some current clustering algorithms. It is proved that the DCAAIN is completely convergent based on the use of Markov chain. The simulation results verified the steady convergence of DCAAIN by comparing with the similar algorithms.
This work was supported in part by the National Natural Science Foundation of China under grant No. 60575006.
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Tong, J., Tan, HZ., Guo, L. (2008). The Convergence Analysis of an Improved Artificial Immune Algorithm for Clustering. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds) New Directions in Intelligent Interactive Multimedia. Studies in Computational Intelligence, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68127-4_19
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DOI: https://doi.org/10.1007/978-3-540-68127-4_19
Publisher Name: Springer, Berlin, Heidelberg
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