Abstract
After analyzing the disadvantages of the classical K-means clustering problem, an improved cuckoo search algorithm (ICS) is applied to cluster analysis, and this paper proposes a novel hybrid clustering algorithm based on Genetic algorithm (GA). The hybrid algorithm includes two modules. At the initial stage, the cuckoo search algorithm (CS) is executed, the clusters’ result are used to the crossover and mutation of genetic algorithm for local search. Comparision of the performance of the proposed approach with the cluster method based on CS and GA algorithm are experimented. The experimental result show the proposed meth has not only higher accuracy bust also higher level of stability. And the faster convergence speed can also be validated by statistical results.
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Acknowledgments
This work is supported by the Project of Guangxi High School Science Foundation under Grant no. KY2015YB539, Project of Guangxi Education Science Foundation under Grant no. 2013C118.
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Li, X., Zheng, H. (2017). A Hybrid Approach Based on CS and GA for Cluster Analysis. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_42
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DOI: https://doi.org/10.1007/978-3-319-63315-2_42
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