Meteorological data analysis using MapReduce

Wei Fang, V. S. Sheng, Xuezhi Wen, Wubin Pan

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Article number646497
JournalThe Scientific World Journal
Volume2014
DOIs
StatePublished - 2014

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