Meteorological data analysis using MapReduce

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Abstract

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. © 2014 Wei Fang et al.

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APA

Fang, W., Sheng, V. S., Wen, X., & Pan, W. (2014). Meteorological data analysis using MapReduce. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/646497

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