An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing

An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing

With the rapid development of urban construction, the number of urban tunnels is increasing and the data they produce become more and more complex. It results in the fact that the traditional clustering algorithm cannot handle the mass data of the tunnel. To solve this problem, an improved parallel...

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Main Authors: Luo Zhong, KunHao Tang, Lin Li, Guang Yang, JingJing Ye
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/630986
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spelling doaj-4ebfda1ebc6746cba7f5c253b39dd1ee2020-11-25T01:53:46ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/630986630986An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud ComputingLuo Zhong0KunHao Tang1Lin Li2Guang Yang3JingJing Ye4Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, ChinaDepartment of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, ChinaDepartment of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, ChinaDepartment of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, ChinaDepartment of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, ChinaWith the rapid development of urban construction, the number of urban tunnels is increasing and the data they produce become more and more complex. It results in the fact that the traditional clustering algorithm cannot handle the mass data of the tunnel. To solve this problem, an improved parallel clustering algorithm based on k-means has been proposed. It is a clustering algorithm using the MapReduce within cloud computing that deals with data. It not only has the advantage of being used to deal with mass data but also is more efficient. Moreover, it is able to compute the average dissimilarity degree of each cluster in order to clean the abnormal data.http://dx.doi.org/10.1155/2014/630986
collection DOAJ
language English
format Article
sources DOAJ
author Luo Zhong
KunHao Tang
Lin Li
Guang Yang
JingJing Ye
spellingShingle Luo Zhong
KunHao Tang
Lin Li
Guang Yang
JingJing Ye
An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
The Scientific World Journal
author_facet Luo Zhong
KunHao Tang
Lin Li
Guang Yang
JingJing Ye
author_sort Luo Zhong
title An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
title_short An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
title_full An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
title_fullStr An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
title_full_unstemmed An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
title_sort improved clustering algorithm of tunnel monitoring data for cloud computing
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2014-01-01
description With the rapid development of urban construction, the number of urban tunnels is increasing and the data they produce become more and more complex. It results in the fact that the traditional clustering algorithm cannot handle the mass data of the tunnel. To solve this problem, an improved parallel clustering algorithm based on k-means has been proposed. It is a clustering algorithm using the MapReduce within cloud computing that deals with data. It not only has the advantage of being used to deal with mass data but also is more efficient. Moreover, it is able to compute the average dissimilarity degree of each cluster in order to clean the abnormal data.
url http://dx.doi.org/10.1155/2014/630986
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AT kunhaotang animprovedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT linli animprovedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT guangyang animprovedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT jingjingye animprovedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT luozhong improvedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT kunhaotang improvedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT linli improvedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT guangyang improvedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
AT jingjingye improvedclusteringalgorithmoftunnelmonitoringdataforcloudcomputing
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