Abstract:
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.
Key words:
K-Means algorithm,
clustering center,
genetic algorithm
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。
关键词:
K均值算法,
聚类中心,
遗传算法
CLC Number:
LAI Yu-xia; LIU Jian-ping; YANG Guo-xing. K-Means Clustering Analysis Based on Genetic Algorithm[J]. Computer Engineering, 2008, 34(20): 200-202.
赖玉霞;刘建平;杨国兴. 基于遗传算法的K均值聚类分析[J]. 计算机工程, 2008, 34(20): 200-202.