Semi-supervised Affinity Propagation Clustering Algorithm Based On Kernel Function

	Semi-supervised Affinity Propagation Clustering Algorithm Based On Kernel Function
	Zhao Xiaoqiang 1,2; Xie Yaping 1
	2015
关键词	Data Mining Clustering Kernel Fraction Affinity Propagation Algorithm
页码	3275-3279
英文摘要	Aiming at complex data sets, affinity propagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinity propagation clustering algorithm based on kernel function (K-SAP Clustering Algorithm) is proposed in this paper. This algorithm first maps the complex clustering space into the feature space and change the similarity measure by a kernel function. Then semi-supervised algorithm is used to adjust the similarity matrix to be neighbours of data in same cluster. Finally, AP algorithm is used to iterate and update to get, the global optimum. Simulation results show the proposed algorithm is better and more accurate than SAP algorithm for complex data sets clustering.
会议录	2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	中文
WOS研究方向	Automation & Control Systems ; Engineering
WOS记录号	WOS:000375232904114
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36540]
专题	电气工程与信息工程学院
通讯作者	Zhao Xiaoqiang
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China 2.Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Zhao Xiaoqiang,Xie Yaping. Semi-supervised Affinity Propagation Clustering Algorithm Based On Kernel Function[C]. 见:.