title: An improved clustering algorithm of tunnel monitoring data for cloud computing.
abstract: 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.
authors: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China.
authors: Luo
authors: L
authors: Zhong
authors: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China ; Department of Computer and Information Science, Hunan Institute of Technology School, Hunan 421002, China.
authors: KunHao
authors: K
authors: Tang
authors: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China ; Collaborative Innovation Center of Hubei Province for Information Technology & Service of Elementary Education, Hubei 430070, China.
authors: Lin
authors: L
authors: Li
authors: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China.
authors: Guang
authors: G
authors: Yang
authors: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 4300702, China.
authors: JingJing
authors: J
authors: Ye
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical