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...
| Publicado en: | Scientific World Journal pp. 630986 - 630987 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103967635&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103967635 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103967635 103967635 NLM24982971 2012634797 10.1155/2014/630986 NLM24982971 PMC3996860 103967635 ppf: 630986 ppct: 1 formats: tig: atl: An improved clustering algorithm of tunnel monitoring data for cloud computing. aug: au: Zhong, Luo Tang, KunHao Li, Lin Yang, Guang Ye, JingJing affil: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China. sug: subj: Clustering Algorithms Models, Theoretical Cloud Computing ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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