一种结合人工蜂群和K-均值的混合聚类算法OA北大核心CSCDCSTPCD
Hybrid clustering algorithm based on artificial bee colony and K-means algorithm
传统的K-均值聚类算法虽然收敛速度快,但由于过度依赖初始聚类中心,算法的鲁棒性较差.为此,提出了一种改进人工蜂群算法与K-均值相结合的混合聚类方法,将改进人工蜂群算法能调节全局寻优能力与局部寻优能力的优点与K-均值算法收敛速度快的优点相结合,来提高算法的鲁棒性.实验表明,该算法不仅克服了传统K-均值聚类算法稳定性差的缺点,而且聚类效果也有了明显改善.
The traditional K-means clustering algorithm is too dependent on the initial clustering centers. With regards to this, this paper proposed a mixed clustering method based on the improvement artificial colony algorithm and the K-means algorithm. The new method combined the advantages of regulating ability of global optimization and local optimization with rapid convergence of K-means clustering algorithm to improve the robustness of the algorithm. Experiments…查看全部>>
毕晓君;宫汝江
哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001
信息技术与安全科学
人工蜂群聚类算法K-均值
artificial bee colonyclustering algorithmK-means
《计算机应用研究》 2012 (6)
2040-2042,2046,4
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