计算机与数字工程2019，Vol.47Issue(5)：1042-1048,7.DOI:10.3969/j.issn.1672-9722.2019.05.006
一种基于改进差分进化的K-均值聚类算法研究
Research on a K-Means Clustering Algorithm Based on Improved Differential Evolution
摘要
Abstract
The difference and the shortcomings of K-means algorithm are sensitive to the initial value and easily fall into the local optimal solution. The difference evolution algorithm has strong global convergence ability and robustness,but its convergence rate is slow. In view of the above problems and defects,this paper first introduces the steps and the concrete process of evolutionary algorithm key operation and differential evolution algorithm. Then,the description,steps and concrete flow of K-means clustering algorithm based on differential evolution are described. Finally,a K-means clustering algorithm based on improved differential evo?lution is proposed,and the improvement scheme,the steps and the concrete flow of the algorithm are introduced in detail. The K-means clustering algorithm based on differential evolution and improved algorithm is used to simulate the experiment. The experi?mental results show that the algorithm has better searching ability,and the algorithm is faster and more robust.关键词
差分进化/K-means算法/K-均值聚类算法Key words
differential evolution/K-means algorithm/K-means clustering algorithm分类
信息技术与安全科学引用本文复制引用
王凤领,梁海英,张波..一种基于改进差分进化的K-均值聚类算法研究[J].计算机与数字工程,2019,47(5):1042-1048,7.基金项目
贺州学院教授科研启动基金项目(编号:HZUJS201615)资助. （编号:HZUJS201615）
