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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953661/ https://www.ncbi.nlm.nih.gov/pubmed/24790576 http://dx.doi.org/10.1155/2014/646497 |
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author | Fang, Wei Sheng, V. S. Wen, XueZhi Pan, Wubin |
author_facet | Fang, Wei Sheng, V. S. Wen, XueZhi Pan, Wubin |
author_sort | Fang, Wei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3953661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39536612014-04-30 Meteorological Data Analysis Using MapReduce Fang, Wei Sheng, V. S. Wen, XueZhi Pan, Wubin ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014-02-23 /pmc/articles/PMC3953661/ /pubmed/24790576 http://dx.doi.org/10.1155/2014/646497 Text en Copyright © 2014 Wei Fang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Fang, Wei Sheng, V. S. Wen, XueZhi Pan, Wubin Meteorological Data Analysis Using MapReduce |
title | Meteorological Data Analysis Using MapReduce |
title_full | Meteorological Data Analysis Using MapReduce |
title_fullStr | Meteorological Data Analysis Using MapReduce |
title_full_unstemmed | Meteorological Data Analysis Using MapReduce |
title_short | Meteorological Data Analysis Using MapReduce |
title_sort | meteorological data analysis using mapreduce |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953661/ https://www.ncbi.nlm.nih.gov/pubmed/24790576 http://dx.doi.org/10.1155/2014/646497 |
work_keys_str_mv | AT fangwei meteorologicaldataanalysisusingmapreduce AT shengvs meteorologicaldataanalysisusingmapreduce AT wenxuezhi meteorologicaldataanalysisusingmapreduce AT panwubin meteorologicaldataanalysisusingmapreduce |