计算机工程与应用2016，Vol.52Issue(16)：85-89,99,6.DOI:10.3778/j.issn.1002-8331.1412-0057
基于稠密区域的K-medoids聚类算法
Novel K-medoids clustering algorithm based on dense regional block
摘要
Abstract
In view of the traditional K-me doids clustering algorithm is sensitive to the initial center, as well as the shortcoming of high number of iterations, put forward a feasible initialization method and a center search update strategy. New algorithm firstly using the density-reachable thought to establish a dense regional block for each object of the data set, select K dense regional blocks which their densities are larger and the distance are far away for each selected dense regional blocks, put the core object of the corresponding dense regional blocks as the K initial centers;Secondly, the centers search update scope is locking the K selected effective dense regional blocks. Tested on Iris, Wine and PId standard data sets, this new algorithm obtains ideal initial centers and dense regional blocks, what’s more, converges to the optimal solution or approximate optimum solution within less number of iterations.关键词
K-me doids聚类算法/稠密区域/初始中心点/中心点搜索更新Key words
K-me doids clustering algorithm/dense regional block/initial center/center search update分类
信息技术与安全科学引用本文复制引用
赵湘民,陈曦,潘楚..基于稠密区域的K-medoids聚类算法[J].计算机工程与应用,2016,52(16):85-89,99,6.基金项目
国家自然科学基金（青年）资助项目（No.61402056，No.61303043）；湖南省研究生科研创新项目（No.CX2014B386）。 （）
