Fast density clustering strategies based on the k-means algorithm

doi:10.1016/j.patcog.2017.06.023

	Fast density clustering strategies based on the k-means algorithm
	Bai, Liang 1,2,3; Cheng, Xueqi 2; Liang, Jiye 1; Shen, Huawei 2; Guo, Yike 3
	2017-11-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	71 页码:375-386
摘要	Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this study, we investigate the application of the k-means algorithm, which is a fast clustering technique, to enhance the scalability of the CFSFDP algorithm while maintaining its clustering results as far as possible. Toward this end, we propose two strategies. First, based on concept approximation, an acceleration algorithm (CFSFDP+A) involving fewer distance calculations is proposed to obtain the same clustering results as those of the original algorithm. Second, to further expand the scalability of the original algorithm, an approximate algorithm (CFSFDP+DE) based on exemplar clustering is proposed to rapidly obtain approximate clustering results of the original algorithm. Finally, experiments are conducted to illustrate the effectiveness and scalability of the proposed algorithms on several synthetic and real data sets. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Cluster analysis Density-based clustering Acceleration mechanism Approximate algorithm k-means
DOI	10.1016/j.patcog.2017.06.023
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61305073] ; National Natural Science Foundation of China[61432011] ; National Natural Science Foundation of China[61573229] ; National Natural Science Foundation of China[U1435212] ; National Natural Science Foundation of China[61403238] ; National Key Basic Research and Development Program of China (973)[2013CB329404] ; National Key Basic Research and Development Program of China (973)[2014CB340400] ; Foundation of Doctoral Program Research of Ministry of Education of China[20131401120001] ; Technology Research Development Projects of Shanxi[2015021100] ; Technology Research Development Projects of Shanxi[201601D202036] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2015107]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000406987400029
出版者	ELSEVIER SCI LTD
引用统计	被引频次：117[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/2XEOYT63/6660
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bai, Liang
作者单位	1.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Imperial Coll London, Dept Comp, SW7, London, England
推荐引用方式 GB/T 7714	Bai, Liang,Cheng, Xueqi,Liang, Jiye,et al. Fast density clustering strategies based on the k-means algorithm[J]. PATTERN RECOGNITION,2017,71:375-386.
APA	Bai, Liang,Cheng, Xueqi,Liang, Jiye,Shen, Huawei,&Guo, Yike.(2017).Fast density clustering strategies based on the k-means algorithm.PATTERN RECOGNITION,71,375-386.
MLA	Bai, Liang,et al."Fast density clustering strategies based on the k-means algorithm".PATTERN RECOGNITION 71(2017):375-386.