Research and Improvement on K-Means Clustering Algorithm

摘要：

　　According to the defects of 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 put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time.The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well,had a better searching ability,and more effectively improved clustering quality and convergence speed.

关键词： differential evolution algorithm K-means cluster algorithm Cluster analysis

作者: Xue-mei Wang Jin-bo Wang

作者单位: Department of Computer Science and Technology, ChengDong College of Northeast Agricultural Universit Liaoning Co., Ltd of China Mobile Group Harbin, 150001,China

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer and Information Applications(ICCIA2012)(2012第二届计算机和信息应用国际会议)

会议地点: 太原

会议语种:英文

页码: 1138-1141

在线出版日期: 2012-12-08（万方平台首次上网日期，不代表论文的发表时间）