计算机工程与应用2018，Vol.54Issue(7)：152-158,7.DOI:10.3778/j.issn.1002-8331.1610-0342
类簇数目和初始中心点自确定的K-means算法
K-means algorithm of clustering number and centers self-determination
摘要
Abstract
K-means algorithm is a classical clustering algorithm based on partition.However,it is difficult for K-means to determine the number of clustering.Besides,K-means is sensitive to the initial centers of clustering.In order to solve the two defects of K-means algorithm, an improved K-means algorithm is proposed. Main work of this paper is putting forward a new method of calculating the density of the object,and using residual analysis method to automatically obtain the initial centers and number of clustering from the decision diagram.The result of experiment shows that the algorithm can get better clustering results.关键词
聚类/局部密度/决策图/残差分析Key words
clustering/local density/decision diagram/residual analysis分类
信息技术与安全科学引用本文复制引用
贾瑞玉,李玉功..类簇数目和初始中心点自确定的K-means算法[J].计算机工程与应用,2018,54(7):152-158,7.基金项目
国家科技支撑计划项目(No.2015BAK24B01) （No.2015BAK24B01）
徽文化传播智能交互技术集成与应用示范项目. （）
