Improved K-means Clustering Based on Genetic Algorithm

摘要：

The K-means algorithm is widely used because of its reliable theory, simple algorithm, fast convergence and it can effectively handle large data sets. However, the traditional K-means algorithm is sensitive to the initial cluster centers; make the average of all objects in the same class as cluster centers, so clustering results is largely affected by the isolated points. To address the problems, search the initial cluster centers of K-means algorithm used of genetic algorithms, improve the K-means algorithm to reduce the impact of isolated points, the data showed that it has good results.

关键词： K-means algorithm initial cluster center genetic algorithms

作者: Wang Min Yin Siqing

作者单位: North University of China School of Electronics and Computer Science and Technology Taiyuan City, Sh North University of China Software School Taiyuan City, Shanxi Province, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 636-639

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）