摘要
结合人工鱼群算法的全局寻优优点提出了一种基于人工鱼群算法的K-平均混合聚类分析算法。实验结果表明,该算法能克服K-平均聚类算法易陷入局部极小的不足,有较好的全局性,且聚类正确率明显高于K-平均算法,聚类效果更好。
This paper proposed a novel hybrid algorithm for clustering analysis based on artificial fish-school algorithm and K-means.The experimental results show that the algorithm can overcome K-means clustering algorithm easily into the local minimum,have a better global optimization.The cluster accuracy is higher than K-means clustering algorithm obviously,and the cluster effect is better.
出处
《计算机应用研究》
CSCD
北大核心
2009年第3期879-880,共2页
Application Research of Computers
基金
国家民委科学基金资助项目(05GX06)
广西自然科学基金资助项目(桂科自0728054)
关键词
人工鱼群算法
K-平均
全局优化
artificial fish-school algorithm
K-means
global optimization
