摘要
针对传统的K-均值聚类算法存在对初始聚类中心点选择敏感、全局搜索能力差和易陷入局部最优等缺点,论文引进一种基于种群的启发式全局优化算法——差分进化算法,并将改进后的差分进化算法和K-均值聚类算法相结合。实验结果表明,该算法较好地解决了K-均值聚类算法初始中心的优化问题,防止算法陷入局部最优解,有较好的搜索能力,有效提高了聚类质量和收敛速度。
According to the defects 05 classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then an improved differential evolution algorithm combined with k- means clustering algorithm is putted forward at the same time. The experiments showed that the method has solved initial centers optimiza- tion problem of k-means clustering algorithm well, a better searching ability,and more effectively improved clustering quality and convergence speed.
出处
《计算机与数字工程》
2013年第11期1717-1719,1759,共4页
Computer & Digital Engineering
基金
国家教师科研专项基金(编号:CTF120771)资助
关键词
差分进化算法
K-均值聚类算法
聚类分析
differential evolution algorithm-DE, K-means cluster algorithm, cluster analysis
