Meteorological data analysis using MapReduce.
In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means) based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability.
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China ; School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Wei
W
Fang
Computer Science Department, University of Central Arkansas, Conway, AR 72035, USA.
V S
VS
Sheng
School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
XueZhi
X
Wen
School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Wubin
W
Pan
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D055904:Meteorology / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results