硕士生导师
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学历:博士研究生毕业
学位:工学博士学位
办公地点:犀浦3号教学楼31529
毕业院校:四川大学
学科:电子信息. 软件工程. 计算机应用技术
所在单位:计算机与人工智能学院
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Particle Subswarms Collaborative Clustering
影响因子:4.747
DOI码:10.1109/TCSS.2019.2940740
所属单位:西南交通大学
发表刊物:IEEE Transactions on Computational Social Systems
刊物所在地:US
关键字:Collaborative clustering, crisp clustering, distributed data, fuzzy clustering, particle swarm optimization (PSO)
摘要:Collaborative clustering aims to find a common data structure between several distributed data sets governed by different privacy constraints and technical limitations that prohibit a central collection of data for processing. Therefore, it is required to process the data sets separately using collaboration, which allows clustering algorithms to work locally on an individual data set while exchanging information about the finding with algorithms in other data locations. Thus, the different data locations share information to improve individual clustering result amidst technical and privacy limitations but without breaching privacy. In this article, we present a framework of collaborative clustering that does not require interaction coefficients to regulate the effect of collaboration. We further adapt the framework to cluster distributed data using crisp and fuzzy clustering algorithms. We use particle swarm optimization techniques to inference the framework and, therefore, call it particle subswarms. Moreover, the collaboration increases the number of particles in the swarm without increasing the number of clusters in the data set. This article, therefore, provides the theoretical foundations of particle subswarms and some experimental results on several data sets.
合写作者:Ji Zhang, Ping Deng,李天瑞
第一作者:Collins Census
论文类型:学术论文
通讯作者:Hongjun Wang
论文编号:20194007492148
学科门类:工学
一级学科:计算机科学与技术
卷号:Volume: 6
期号:Issue: 6, December 2019
页面范围:1165 - 1179
ISSN号:2329-924X
是否译文:否
发表时间:2019-09-27