%0 Journal Article
%T Improved adaptive affinity propagation clustering based on semi-supervised learning<br>一种结合半监督的改进自适应亲和传播聚类
%A 王磊
%A 汪西莉
%A 刘高霞
%A 赵琳
%J 计算机应用研究
%D 2010
%I 
%X The existing adaptive affinity propagation clustering has some shortcoming,such as long runtime and low accuracy. This paper proposed an improved adaptive affinity propagation clustering based on semi-supervised learning (SAAP).It first updated the similarity matrix by semi-supervised learning, then scaned adaptively the effective clustersing space based on Affinity propagation clustering by the dichotomy judge,and finally determined the best clustering by the weighted evaluation function.The experiments show, SAAP algorithm can more quickly scan effective clustering space, and can be smaller misclassification rate and a higher effectiveness evaluation index.
%K affinity propagation(AP)
%K semi-supervised clustering
%K adaptive clustering
%K pairwise constraints<br>亲和传播
%K 半监督聚类
%K 自适应聚类
%K 成对约束
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=091577FDEBB459FA1281AF3549705BBA&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=59906B3B2830C2C5&sid=97329015592792FB&eid=BEDA9700B739A038&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10