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Fast affinity propagation clustering based on incomplete similarity matrix
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文献类型:期刊论文
标题:Fast affinity propagation clustering based on incomplete similarity matrix
作者:Sun, Leilei[1];Guo, Chonghui[1];Liu, Chuanren[2];Xiong, Hui[3]
机构:
[1]Dalian Univ Technol, Inst Syst Engn, Dalian, Liaoning, Peoples R China.;
[2]Drexel Univ, Decis Sci & MIS Dept, Philadelphia, PA 19104 USA.;
[3]Rutgers State Univ, Management Sci & Informat Syst Dept, Newark, NJ USA.;
通讯作者:Guo, CH (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian, Liaoning, Peoples R China.
年:2017
来源信息:年:2017  卷:51  期:3  页码范围:941-963  
期刊信息:KNOWLEDGE AND INFORMATION SYSTEMS影响因子和分区  ISSN:0219-1377
卷:51
期:3
页码范围:941-963
增刊:正刊
学科:计算机科学
收录情况:SCIE(WOS:000400757700007)  
所属部门:经济管理学院
语言:外文
ISSN:0219-1377
发表时间:2017-06-01
全文链接:DOI百度学术
被引频次:8
人气指数:113
浏览次数:113
关键词:Exemplar-based clustering; Affinity propagation; Incomplete similarity matrix; Fast algorithm
摘要:Affinity propagation (AP) is a recently proposed clustering algorithm, which has been successful used in a lot of practical problems. Although effective in finding meaningful clustering solutions, a key disadvantage of AP is its efficiency, which has become the bottleneck when applying AP for large-scale problems. In the literature, most of the methods proposed to improve the efficiency of AP are based on implementing the message-passing on a sparse similarity matrix, while neither the decline in effectiveness nor the improvement in efficiency is theoretically analyzed. In this paper, we propose a two-stage fast affinity propagation (FastAP) algorithm. Different from previous work, the scale of the similarity matrix is first compressed by selecting only potential exemplars, then further reduced by sparseness according to k nearest neighbors. More importantly, we provide theoretical analysis, based on which the improvement of efficiency in our method is controllable with guaranteed clustering performance. In experiments, two synthetic data sets, seven publicly available data sets, and two real-world streaming data sets are used to evaluate the proposed method. The results demonstrate that FastAP can achieve comparable clustering performances with the original AP algorithm, while the computational efficiency has been improved with a several-fold speed-up on small data sets and a dozens-of-fold on larger-scale data sets.
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