An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing
Joint Authors
Zhong, Luo
Tang, KunHao
Li, Lin
Yang, Guang
Ye, JingJing
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-02
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zhong, Luo& Tang, KunHao& Li, Lin& Yang, Guang& Ye, JingJing. 2014. An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1050422
Modern Language Association (MLA)
Zhong, Luo…[et al.]. An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing. The Scientific World Journal No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1050422
American Medical Association (AMA)
Zhong, Luo& Tang, KunHao& Li, Lin& Yang, Guang& Ye, JingJing. An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1050422
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050422