摘要
提出了一种基于改进蚁群算法的动态K-均值聚类算法思想,该算法首先利用蚁群算法的较强处理局部极值的能力,动态地确定了聚类数目和中心,然后利用蚁群聚类得到的结果,再进行K-均值聚类弥补蚁群算法的不足。两者有机结合起来可以寻求到具有全局分布特性的最优聚类,实现了基于改进的蚁群聚类算法分析。
This paper proposes a method of dynamic K-mean clustering analysis based on ant colony algorithm. The algorithm makes use of the great ability of ant colony algorithm for disposing local extremum firstly. And then the results from previous for K-mean clustering method can make up the deficiency of ant colony algorithm. In this way, we combine ant colony algorithm with K-means clustering organically and find the whole distributing optimization clustering.
出处
《教育技术导刊》
2008年第1期154-155,共2页
Introduction of Educational Technology
关键词
蚁群算法
K-均值聚类
动态K-均值聚类算法
ant colony algorithm
K-means clustering
Dynamic K-mean clustering algorithm
