一种K-均值聚类的改进算法及其应用  

A K-means algorithm based on density and its application

作　　者：江京亚 郭庆胜[1] 陈旺[1] 周贺杰 陈勇 

机构地区：[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]湖北省鄂东地质大队,湖北孝感432000

出　　处：《测绘工程》2015年第5期42-46,共5页Engineering of Surveying and Mapping

基　　金：国家863计划资助项目(2012AA12A402);国家自然科学基金资助项目(41071289;41171350)

摘　　要：由于传统的K-均值聚类算法固有的特性,如对初始聚类中心的依赖性和对噪声点的敏感性,导致了其聚类结果的不稳定。文中基于k-dist图提出一种改进算法,算法首先去除数据集中的噪声点,然后从数据集中选取靠近点聚集区域、相距最远的k′个数据点作为初始簇中心。实验结果证明,文中算法能够很好地消除K-均值聚类算法对初始簇中心的依赖性,并能有效去除噪声点。Because of the inherent characteristics of the traditional K‐means algorithm ,such as the dependence of the initial clustering center and the sensitivity to noise points ,it is easy to generate the instable clustering results .An improved algorithm is proposed based on the k‐dist graph .The algorithm first removes the noise points of the data set ,and then selects k′initial center ,w hich is close to the point gathering area and far away from each other .The experimental results show that the algorithm can eliminate its dependence of the initial center as well as removing the noise points effectively .

关 键 词：数据挖掘 K-均值聚类 第4邻近距离图 初始簇中心 噪声点 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]