摘要
传统K均值算法对初始聚类中心敏感,聚类结果随不同的初始输入而波动,容易陷入局部最优值。针对上述问题,该文提出一种基于遗传算法的K均值聚类算法,将K均值算法的局部寻优能力与遗传算法的全局寻优能力相结合,在自适应交叉概率和变异概率的遗传算法中引入K均值操作,以克服传统K均值算法的局部性和对初始中心的敏感性,实验证明,该算法有较好的全局收敛性,聚类效果更好。
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value. To solve such problems, this paper presents an improved K-Means algorithm based on genetic algorithm. It combines the locally searching capability of the K-Means with the global optimization capability of genetic algorithm, and introduces the K-Means operation into the genetic algorithm of adaptive crossover probability and adaptive mutation probability, which overcomes the sensitivity to the initial start centers and locality of K-Means. Experimental results demonstrate that the algorithm has greater global searching capability and can get better clustering.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第20期200-202,共3页
Computer Engineering
关键词
K均值算法
聚类中心
遗传算法
K-Means algorithm
clustering center
genetic algorithm
