%0 Journal Article
%T Kernel-based adaptation for affinity propagation clustering algorithm<br>基于核自适应的近邻传播聚类算法
%A FU Ying-ding
%A LAN Ju-long
%A <br>付迎丁
%A 兰巨龙
%J 计算机应用研究
%D 2012
%I 
%X AP algorithm has become increasingly popular in recent years as an efficient and fast clustering algorithm.AP has better performance on large and multi-class dataset than the existing clustering algorithms.But for the datasets with complex cluster structures,it cannot produce good clustering results.Through analyzing the property of data clusters,this paper proposed a kernel function,optimized that the parameters automatically according to the dataset structure,and the dataset in kernel space were linearly separable or almost linearly.Carried AP on the kernel space,it had a kernel-adaptive affinity propagation clustering algorithm(KA-APC).Compared with the original AP clustering,it had the advantages of effectively dealing with the large multi-scale dataset.The promising experimental results show that this algorithm outperforms the original AP algorithm.
%K affinity propagation(AP)
%K kernel clustering
%K kernel adaptive clustering
%K manifold learning<br>近邻传播聚类
%K 核聚类
%K 核自适应聚类
%K 流形学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC54E18ECD0CFB5BEE8&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=9036AC33107DC7C4&eid=3C4387F16C0A0126&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10