Adaptive K-valued K-means clustering algorithm

Wang Shenghui, Liang Hanbing
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引用次数: 3

Abstract

Based on particle swarm optimization (PSO), the algorithm for selecting appropriate K values is improved by combining k-mean algorithm. When the algorithm converges, the expansion and reduction of K value can be determined by comparing the relationship between different K value selection and global optimal fitness. Experiments show that the improved algorithm can assist K value selection effectively and obtain a better clustering effect.
自适应k值k均值聚类算法
在粒子群算法(PSO)的基础上,结合K均值算法对K值选取算法进行了改进。当算法收敛时,可以通过比较不同K值选择与全局最优适应度之间的关系来确定K值的展开和缩小。实验表明,改进后的算法可以有效地辅助K值的选择,获得更好的聚类效果。
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