中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Fast density clustering strategies based on the k-means algorithm

文献类型：期刊论文

作者Bai, Liang1,2,3; Cheng, Xueqi2; Liang, Jiye1; Shen, Huawei2; Guo, Yike3
刊名PATTERN RECOGNITION
出版日期2017-11-01
卷号71页码:375-386
关键词Cluster analysis Density-based clustering Acceleration mechanism Approximate algorithm k-means
ISSN号0031-3203
DOI10.1016/j.patcog.2017.06.023
英文摘要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.
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
语种英语
WOS记录号WOS:000406987400029
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/6659]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。