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

doi:10.1016/j.knosys.2016.02.001

	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
	2016-05-01
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
卷号	99 页码:135-145
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.
关键词	Data clustering Density peaks k Nearest neighbors (KNN) Principal component analysis (PCA)
DOI	10.1016/j.knosys.2016.02.001
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000374603400013
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：334[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8545
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.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100090, 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.