计算机与数字工程Issue(3)：379-382,4.DOI:10.3969/j.issn1672-9722.2015.03.008
基于均值与最大距离乘积的初始聚类中心优化 K-means 算法*
Automatic Generation Cloud Optimiz ation Based on Genetic Algorithm
摘要
Abstract
Aiming at solving the problem of clustering results randomness ,low stability ,easy to fall into local optimum and no global optimal solution of K‐means algorithm randomly chosen initial cluster centers ,a kind of initial cluster center optimization K‐means algorithm based on the product of the mean and maximum distance is put forward .Firstly ,the farthest distance mean sample set of data objects are chosen to join the cluster center set ,then the sample mean and maximum current cluster center product data object are set in turn to join the cluster center collection .Experimental results on the standard da‐ta sets show that ,compared with the original K‐means algorithm and another improved algorithm ,the proposed new cluste‐ring algorithm has a higher accuracy rate .关键词
K-means聚类算法/均值/最大距离乘积/数据挖掘Key words
K-means clustering algorithm/mean/maximum distance product/data mining分类
信息技术与安全科学引用本文复制引用
段桂芹..基于均值与最大距离乘积的初始聚类中心优化 K-means 算法*[J].计算机与数字工程,2015,(3):379-382,4.基金项目
2013年广东省高职教育教学指导委员会教改项目（编号XXJS-2013-2041）；广东松山职业技术学院技术应用重点课题（编号2012-JYKY-19）资助。 （）
