Improved K-means Clustering Algorithm

摘要：

K-means algorithm is widely used in spacial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages: sensitive to the initial centroid and instability. The improved clustering algorithm referred to the best clustering centriod which is searched during the optimization of clustering centroid. That increased the searching probability around the best centroid and improved the stability of the algorithm. The experiment on two groups of representative dataset proved that the improved K-means algorithm performs better in global searching and is less sensitive to the initial centroid.

关键词： GIS spatial data mining integration customer relationship management

作者: Junxi Zhang Zhe Zhang Huifeng Xue

作者单位: College of Automation, Northwestern Polytechnical University, Xian 710072, China

会议类型: 国际会议

会议名称: 北京国际地理信息系统学术讨论会第七届会议（7th International Workshop Geographical Information System

会议地点: 北京

会议语种:英文

页码: 126-129

在线出版日期: 2007-09-14（万方平台首次上网日期，不代表论文的发表时间）