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Self-adaption neighborhood density clustering method for mixed data stream with concept drift

文献类型：期刊论文

作者Xu, Shuliang2; Feng, Lin3; Liu, Shenglan3; Qiao, Hong1,4
刊名ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
出版日期2020-03-01
卷号89页码:14
关键词Data stream Concept drift Rough set Clustering analysis Neighborhood entropy
ISSN号0952-1976
DOI10.1016/j.engappai.2019.103451
通讯作者Feng, Lin(fenglin@dlut.edu.cn)
英文摘要Clustering analysis is an important data mining method for data stream. In this paper, a self-adaption neighborhood density clustering method for mixed data stream is proposed. The method uses a significant metric criteria to make categorical attribute values become numeric and then the dimension of data is reduced by a nonlinear dimensionality reduction method. In the clustering method, each point is evaluated by neighborhood density. The k points are selected from the data set with maximum mutual distance after k is determined according to rough set. In addition, a new similarity measure based on neighborhood entropy is presented. The data points can be partitioned into the nearest cluster and the algorithm adaptively adjusts the clustering center points by clustering error. The experimental results show that the proposed method can obtain better clustering results than the comparison algorithms on the most data sets and the experimental results prove that the proposed algorithm is effective for data stream clustering.
WOS关键词ROUGH ; SELECTION
资助项目National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Fund of China[61972064] ; National Natural Science Fund of China[61672130] ; National Natural Science Fund of China[61602082] ; National Natural Science Fund of China[61627808] ; National Natural Science Fund of China[91648205] ; Open Program of State Key Laboratory of Software Architecture, China[SKLSAOP1701] ; Liaoning Revitalization Talents Program, China[XLYC1806006] ; Fundamental Research Funds for the Central Universities, China[DUT19RC(3)012] ; Fundamental Research Funds for the Central Universities, China[DUT17RC(3)071] ; development of science and technology of Guangdong province special fund project, China[2016B090910001]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:000515429100017
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key Research and Development Program of China ; National Natural Science Fund of China ; Open Program of State Key Laboratory of Software Architecture, China ; Liaoning Revitalization Talents Program, China ; Fundamental Research Funds for the Central Universities, China ; development of science and technology of Guangdong province special fund project, China
源URL[http://ir.ia.ac.cn/handle/173211/38350]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Feng, Lin
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China
3.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116024, Peoples R China
4.State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Shuliang,Feng, Lin,Liu, Shenglan,et al. Self-adaption neighborhood density clustering method for mixed data stream with concept drift[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2020,89:14.
APA Xu, Shuliang,Feng, Lin,Liu, Shenglan,&Qiao, Hong.(2020).Self-adaption neighborhood density clustering method for mixed data stream with concept drift.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,89,14.
MLA Xu, Shuliang,et al."Self-adaption neighborhood density clustering method for mixed data stream with concept drift".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 89(2020):14.

入库方式： OAI收割

来源：自动化研究所

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