Abstract
Cluster Analysis which plays an important role in Data Mining, is widely used. It has important value both in theory and application. Considering the stability of the Genetic Algorithm and the local searching capability of Particle Swarm Optimization in clustering, those two algorithms are combined. Particle Swarm Optimization operators are implemented after the crossover and mutation operators, and GA-PSO clustering algorithm is put forwarded. Simulation results are given to illustrate the stability and convergence of the proposed method. GA-PSO is proved to be easier to carry out, faster to converge and more stable than other methods.
Supported by the Fundamental Research Funds for the Central Universities(GK201002005), Xi’an Science and Technology Innovation Programming(CXY1016-2), Shaan Xi Industrial research program(No. 2009K09-21).
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Huo, T., Zhang, J., Wu, X. (2011). Cluster Analysis Based on GAPSO Evolutionary Algorithm. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_28
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DOI: https://doi.org/10.1007/978-3-642-24282-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24281-6
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