摘要
针对传统K-Means算法对初始聚类中心较为敏感,易收敛到局部最优的缺点,提出了一种粒子群算法优化的K-Means聚类算法。该算法在K-Means算法的基础上定义了一种不需迭代的分类方式,并将此方式与经典粒子群算法结合,利用粒子群算法强大的全局搜索能力,对初始聚类中心的选取进行优化,进而对数据集进行聚类。实验结果表明该算法与传统K-Means算法相比具有更高的聚类准确率。
Aiming at the shortcomings of traditional K-Means Algorithm which is sensitive to initial clustering centers and easy to converge to local optima,an optimized K-Means Algorithm based on Particle Swarm Optimization(PSO) algorithm is proposed.It takes advantage of the powerful global searching capability of PSO algorithm to improve the selection of the initial centers,and a way without iterations of classification based on K-Means algorithm is defined,and it is combined with the PSO algorithm to cluster data sets.The experiment results show that the proposed method has higher accuracy.
出处
《微型电脑应用》
2015年第10期45-46,5,共2页
Microcomputer Applications
