计算机技术与发展2017，Vol.27Issue(9)：60-63,69,5.DOI:10.3969/j.issn.1673-629X.2017.09.013
基于密度与最小距离的K-means算法初始中心方法
An Initial Center Algorithm of K-means Based on Density and Minimum Distance
摘要
Abstract
In order to overcome a large fluctuation caused by the traditional K-means algorithm clustering with assignment of the random initial cluster centers,an algorithm taken the density and minimum distance as the parameters to determine the initial cluster centers is pro-posed,which calculates the density parameter of the data object according to certain rules and minimum distance between each data object and other data objects after having calculated single point density of each data in the data set. The larger one among the densities and min-imum distances has been chosen as initial cluster center in the process of K-means clustering. Experimental results show that it has higher accuracy and faster convergence rate compared with ones using randomly selected cluster centers and using density as a parameter for K-means clustering on standard UCI data set.关键词
K-means算法/类簇中心/密度/最小距离/迭代次数Key words
K-means algorithm/cluster center/density/minimum distance/iteration number分类
信息技术与安全科学引用本文复制引用
戚后林,顾磊..基于密度与最小距离的K-means算法初始中心方法[J].计算机技术与发展,2017,27(9):60-63,69,5.基金项目
国家自然科学基金资助项目(61302157) （61302157）
