计算机应用与软件Issue(2)：261-263,277,4.DOI:10.3969/j.issn.1000-386x.2015.02.063
改进的二分K均值聚类算法
IMPROVED BISECTING K-MEANS CLUSTERING ALGORITHM
摘要
Abstract
K-means algorithm is a kind of commonly used clustering algorithm based on the prototype.But the algorithm requires the user to randomly select initial centre of mass,which makes the K-means algorithm greatly influenced by the initialisation.Although the bisecting K-means algorithm has ameliorated this issue,but it still requires the user to specify clustering number,which impacts clustering effect.We use hierarchical clustering to improve bisecting K-means algorithm,thus solve the problem of impact caused by the bisecting K-means algorithm being affected by the number of clustering the user specified.Moreover,we combine the Chameleon algorithm and unite the clusters being divided too fine and optimise the clustering results.Simulation experiments prove that the unifying nature and separation property of the improved clustering algorithm is better than the bisecting K-means clustering algorithm.关键词
K均值聚类/二分K均值聚类/Chameleon算法/层次聚类Key words
K-means clustering/Bisect K-means clustering/Chameleon algorithm/Hierarchical clustering分类
信息技术与安全科学引用本文复制引用
刘广聪,黄婷婷,陈海南..改进的二分K均值聚类算法[J].计算机应用与软件,2015,(2):261-263,277,4.基金项目
广州科技计划项目 （）
