摘要
在分析K均值聚类算法存在不足的基础上,该文提出了一种新的聚类算法:基于粒子群的K均值聚类算法。实验结果证明,该算法有很好的全局收敛性,不仅有效地克服了传统的k均值算法易陷入局部极小值和对初始值敏感的问题,而且具有较快的收敛速度。
After analyzing the disadvantages of the classical K-means clustering algorithm,this paper proposes a novel K-means clustering based on Particle Swarm Optimization algorithm.The experimental results show that the algorithm not only avoids the local optima and is robust to initialization,but also increases the convergence speed and has global searching capability.
出处
《计算机工程与应用》
CSCD
北大核心
2005年第20期183-185,共3页
Computer Engineering and Applications
