%0 Journal Article
%T Clustering Ensemble Approaches: An Overview<br>聚类融合方法综述
%A YANG Lin-yun
%A WANG Wen-yuan
%A <br>阳琳贇
%A 王文渊
%J 计算机应用研究
%D 2005
%I 
%X Ensemble approaches are widely and successfully used in classification algorithms and regression models.It can offer better results for overcoming instabilities in classification algorithms and regression models.However,in unsupervised learning,the researches of ensemble approaches are concerned only in recent years.Because the prior information of data sets in unsupervised learning is unknown,the ensemble approaches of classification algorithms and regression models can't be utilized in the same way directly.Recent researches and experiments show that clustering ensemble approaches can enhance the robustness and stabilities of unsupervised learning greatly.This paper makes an overview of the clustering ensemble approaches in recent years.It illustrates the contents and characteristics of recent clustering ensemble approaches research and discusses the future directions of clustering ensemble study.
%K Clustering Ensemble
%K Data Resampling
%K Consensus Function
%K Diversity<br>聚类融合
%K 数据重抽样
%K 共识函数
%K 差异度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=8E2CFE73AF63D0A7&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=59906B3B2830C2C5&sid=5D311CA918CA9A03&eid=F3090AE9B60B7ED1&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=11&reference_num=24