%0 Journal Article
%T Efficient K-medoids clustering algorithm<br>一种高效的K-medoids聚类算法
%A 夏宁霞
%A 苏一丹
%A 覃希
%J 计算机应用研究
%D 2010
%I 
%X Due to the disadvantages of sensitivity to the initial selection of the medoids and poor performance in large data set processing in the K-medoids clustering algorithm, this paper proposed an improved K-medoids algorithm based on a fine-tuned of initial medoids and an incremental candidate set of medoids. The proposed algorithm optimized initial medoids by fine-tu-ning and reduced computational complexity of medoids substitution through expanding medoids candidate set gradually. Expenrimental results demonstrate the effectiveness of this algorithm,which can improve clustering quality and significantly shorten the time in calculation compared with the traditional K-medoids algorithm.
%K clustering
%K K-medoids algorithm
%K medoid fine-tuning
%K incremental candidate<br>聚类
%K K-medoids算法
%K 中心微调
%K 增量候选
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=0E24F2DAF84D15EA241A68660437D8DA&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=59906B3B2830C2C5&sid=64D4A64EBB3B6E7E&eid=512F7318B1A66FE4&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10