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

文献类型：期刊论文

作者Du, Mingjing1,2; Ding, Shifei1,2; Jia, Hongjie1,2
刊名KNOWLEDGE-BASED SYSTEMS
出版日期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
DOI10.1016/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
WOS记录号WOS:000374603400013
出版者ELSEVIER SCIENCE BV
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.

入库方式： OAI收割

来源：计算技术研究所

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