计算机应用研究2011，Vol.28Issue(11)：4188-4190,3.DOI:10.3969/j.issn.1001-3695.2011.11.050
一种有效的K-means聚类中心初始化方法
Effective method for cluster centers' initialization in K-means clustering
摘要
Abstract
Initializing cluster centers randomly, traditional K-means algorithm leads to great fluctuations in the clustering results. The existing max-min distance algorithm, indeed, has rather dense cluster centers, which may easily bring about clustering conflicts. To solve these problems, this paper regarded the existing max-min distance algorithm as the thinking foundation and proposed the maximum distances product algorithm. Based on the theory of density-based clustering, the maximum distances product algorithm selected each point which had maximum product of distances between itself and all other initialized clustering centers. Theory analysis and experimental results show that compared with traditional K-means algorithm and max-min distance algorithm, the maximum distances product algorithm can result in faster convergence speed, higher accuracy, greater stability.关键词
K-均值算法/基于密度/初始聚类中心/最大最小距离/最大距离积Key words
K-means algorithm/ density-based clustering/ initial clustering centers/ max-min distance/ maximum distances product分类
信息技术与安全科学引用本文复制引用
熊忠阳,陈若田,张玉芳..一种有效的K-means聚类中心初始化方法[J].计算机应用研究,2011,28(11):4188-4190,3.基金项目
重庆市科委基金资助项目(2008BB2191) （2008BB2191）
