Study on density peaks clustering based on k-nearest neighbors and principal component analysis

doi:10.1016/j.knosys.2016.02.001

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Study on density peaks clustering based on k-nearest neighbors and principal component analysis
	Du, Mingjing 1,2; Ding, Shifei 1,2; Jia, Hongjie 1,2
刊名	KNOWLEDGE-BASED SYSTEMS
	2016-05-01
卷号	99 页码:135-145
关键词	Data clustering Density peaks k Nearest neighbors (KNN) Principal component analysis (PCA)
ISSN号	0950-7051
DOI	10.1016/j.knosys.2016.02.001
英文摘要	Density peaks clustering (DPC) algorithm published in the US journal Science in 2014 is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, original algorithm only has taken into account the global structure of data, which leads to missing many clusters. In addition, DPC does not perform well when data sets have relatively high dimension. Especially, DPC generates wrong number of clusters of real-world data sets. In order to overcome the first problem, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which introduces the idea of k nearest neighbors (KNN) into DPC and has another option for the local density computation. In order to overcome the second problem, we introduce principal component analysis (PCA) into the model of DPC-KNN and further bring forward a method based on PCA (DPC-KNN-PCA), which preprocesses high dimensional data. By experiments on synthetic data sets, we demonstrate the feasibility of our algorithms. By experiments on real-world data sets, we compared this algorithm with k-means algorithm and spectral clustering (SC) algorithm in accuracy. Experimental results show that our algorithms are feasible and effective. (C) 2016 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[61379101] ; National Key Basic Research Program of China[2013CB329502]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000374603400013
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/8544]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ding, Shifei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100090, Peoples R China 2.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
推荐引用方式 GB/T 7714	Du, Mingjing,Ding, Shifei,Jia, Hongjie. Study on density peaks clustering based on k-nearest neighbors and principal component analysis[J]. KNOWLEDGE-BASED SYSTEMS,2016,99:135-145.
APA	Du, Mingjing,Ding, Shifei,&Jia, Hongjie.(2016).Study on density peaks clustering based on k-nearest neighbors and principal component analysis.KNOWLEDGE-BASED SYSTEMS,99,135-145.
MLA	Du, Mingjing,et al."Study on density peaks clustering based on k-nearest neighbors and principal component analysis".KNOWLEDGE-BASED SYSTEMS 99(2016):135-145.