An effective algorithm based on density clustering framework

doi:10.1109/ACCESS.2017.2688477

CQUET-IR

	An effective algorithm based on density clustering framework
	Lu, Jianyun 1,2; Zhu, Qingsheng 1,3
	2017
发表期刊	IEEE Access
ISSN	2169-3536
卷号	5 页码:4991-5000
摘要	Clustering analysis has the very broad applications on data analysis, such as data mining, machine learning, and information retrieval. In practice, most of clustering algorithms suffer from the effects of noises, different densities and shapes, cluster overlaps, etc. To solve the problems, in this paper, we propose a simple but effective density-based clustering framework (DCF) and implement a clustering algorithm based on DCF. In DCF, a raw data set is partitioned into core points and non-core points by a neighborhood density estimation model, and then the core points are clustered first, because they usually represent the center or dense region of the cluster structure. Finally, DCF classifies the non-core points into initial clusters in sequence. In experiments, we compare our algorithm with Dp and DBSCAN algorithms on synthetic and real-world data sets. The experimental results show that the performance of the proposed clustering algorithm is comparable with DBSCAN and Dp algorithms. © 2013 IEEE.
关键词	Cluster analysis Data mining Nearest neighbor search Trees (mathematics) Cluster structure Clustering analysis Density clustering Density estimation Different densities Effective algorithms Minimum spanning trees Reverse k-nearest neighbors
DOI	10.1109/ACCESS.2017.2688477
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000402940400109
出版者	Institute of Electrical and Electronics Engineers Inc., United States
EI入藏号	20171903642915
EI分类号	723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 903.1 Information Sources and Analysis ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory ; 921.5 Optimization Techniques
原始文献类型	Journal article (JA)
出版地	PISCATAWAY
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3036
专题	重庆电子科技职业大学
作者单位	1.College of Computer Science, Chongqing University, Chongqing; 400044, China; 2.Chongqing College of Electronic and Engineering, Chongqing; 401331, China; 3.Chongqing Key Laboratory of Software Theory and Technology, College of Computer Science, Chongqing University, Chongqing; 400044, China
推荐引用方式 GB/T 7714	Lu, Jianyun,Zhu, Qingsheng. An effective algorithm based on density clustering framework[J]. IEEE Access,2017,5:4991-5000.
APA	Lu, Jianyun,&Zhu, Qingsheng.(2017).An effective algorithm based on density clustering framework.IEEE Access,5,4991-5000.
MLA	Lu, Jianyun,et al."An effective algorithm based on density clustering framework".IEEE Access 5(2017):4991-5000.

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