中国科学院国家空间科学中心机构知识库
Advanced  
NSSC OpenIR  > 空间技术部  > 期刊论文
题名: GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing
作者: Deng, Chao; Song, Jinwei; Sun, Ruizhi; Cai, Saihua; Shi, Yinxue
作者部门: 空间技术部
通讯作者: Sun, Ruizhi (sunruizhi@cau.edu.cn)
关键词: Grid based clustering ; Density based clustering ; DBSCAN ; GRIDEN ; Data mining ; Massive spatial data ; Parallel computing
刊名: Pattern Recognition Letters
ISSN号: 0167-8655
出版日期: 2018
卷号: 109, 页码:81-88
收录类别: SCI ; EI
项目资助者: Chinese Universities Scientific Fund [2017XD001] ; China Tobacco Guangxi Industrial Co., Ltd.
英文摘要: Density-based clustering has been widely used in many fields. A new effective grid-based and density-based spatial clustering algorithm, GRIDEN, is proposed in this paper, which supports parallel computing in addition to multi-density clustering. It constructs grids using hyper-square cells and provides users with parameter k to control the balance between efficiency and accuracy to increase the flexibility of the algorithm. Compared with conventional density-based algorithms, it achieves much higher performance by eliminating distance calculations among points based on the newly proposed concept of Ε-neighbor cells. Compared with conventional grid-based algorithms, it uses a set of symmetric (2k+1)D cells to identify dense cells and the density-connected relationships among cells. Therefore, the maximum calculated deviation of Ε-neighbor points in the grid-based algorithm can be controlled to an acceptable level through parameter k. In our experiments, the results demonstrate that GRIDEN can achieve a reliable clustering result that is infinite closed with respect to the exact DBSCAN as parameter k grows, and it requires computational time that is only linear to N. © 2017 Elsevier B.V.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/6143
Appears in Collections:空间技术部_期刊论文

Files in This Item:
File Name/ File Size Content Type Version Access License
2017S0167865517304178.pdf(2429KB)期刊论文作者接受稿限制开放View 联系获取全文

Recommended Citation:
Deng, Chao,Song, Jinwei,Sun, Ruizhi,et al. GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing[J]. Pattern Recognition Letters,2018,109:81-88.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Deng, Chao]'s Articles
[Song, Jinwei]'s Articles
[Sun, Ruizhi]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Deng, Chao]‘s Articles
[Song, Jinwei]‘s Articles
[Sun, Ruizhi]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: 2017S0167865517304178.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2018  中国科学院国家空间科学中心 - Feedback
Powered by CSpace