A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain

doi:10.1016/j.engappai.2018.03.014

	A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain
	Lu, Jianyun1,3 ; Zhu, Qingsheng 1,2; Wu, Quanwang 1
	2018-06-01
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
EISSN	1873-6769
卷号	72 页码:213-227
摘要	In practice, clustering algorithms usually suffer from the complex structure of the dataset, including data distribution and dimensionality. Meanwhile, the number of clusters, which is required as an input, is usually unavailable. In this paper, we propose a novel data clustering algorithm: it uses heuristic rules based on k-nearest neighbors chain and does not require the number of clusters as the input parameter. Inspired by the PageRank algorithm, we first use random walk model to measure the importance of data points. Then, on the basis of the important data points, we build a K-Nearest Neighbors Chain (KNNC) to order the k nearest neighbors by distance and propose two heuristic rules to find the proper number of clusters and initial clusters. The first heuristic rule is the gap of KNNC which reflects the degree of separation of clusters with convex shapes and the second one is the nearest neighbor gap of KNNC which reflects the inner compactness of a cluster. Comprehensive comparison results on synthetic and real datasets indicate that the proposed clustering algorithm can find the proper number of clusters and achieve comparable or even better performance than the popular clustering algorithms. © 2018 Elsevier Ltd
关键词	Cluster analysis Data mining Motion compensation Nearest neighbor search Optical variables measurement Random processes Structure (composition) Comprehensive comparisons Data clustering algorithm Degree of separation Heuristic rules K-nearest neighbors Number of clusters PageRank algorithm Random walk modeling
DOI	10.1016/j.engappai.2018.03.014
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:000434239000020
出版者	Elsevier Ltd
EI入藏号	20181705042867
EI分类号	723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 903.1 Information Sources and Analysis ; 921.5 Optimization Techniques ; 922.1 Probability Theory ; 941.4 Optical Variables Measurements ; 951 Materials Science
原始文献类型	Journal article (JA)
出版地	OXFORD
引用统计	被引频次：19[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/39TD4454/3165
专题	人工智能与大数据学院
作者单位	1.College of Computer Science, Chongqing University, Chongqing; 400044, China; 2.Chongqing Key Lab. of Software Theory and Technology, College of Computer Science, Chongqing University, Chongqing; 400044, China; 3.Chongqing College of Electronic Engineering, Chongqing; 401331, China
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Lu, Jianyun,Zhu, Qingsheng,Wu, Quanwang. A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2018,72:213-227.
APA	Lu, Jianyun,Zhu, Qingsheng,&Wu, Quanwang.(2018).A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,72,213-227.
MLA	Lu, Jianyun,et al."A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 72(2018):213-227.