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: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/630986 |
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| _version_ | 1832545572892966912 |
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| author | Luo Zhong KunHao Tang Lin Li Guang Yang JingJing Ye |
| author_facet | Luo Zhong KunHao Tang Lin Li Guang Yang JingJing Ye |
| author_sort | Luo Zhong |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-4ebfda1ebc6746cba7f5c253b39dd1ee |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4ebfda1ebc6746cba7f5c253b39dd1ee2025-02-03T07:25:18ZengWileyThe 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 |
| 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 |
| title | 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_short | An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing |
| title_sort | improved clustering algorithm of tunnel monitoring data for cloud computing |
| url | http://dx.doi.org/10.1155/2014/630986 |
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