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Analysis and Research of K-means Algorithm in Soil Fertility Based on Hadoop Platform

Abstract : In order to study the K - means algorithm for evaluation of soil fertility, solve the large amount of calculation and high time complexity of the algorithm,this paper proposes the K-means algorithm based on Hadoop platform. First, K-means algorithm is used to cluster for Nongan town soil nutrient data for nine consecutive years; clustering results show that: the accuracy rate increased year by year, and consistent with the actual situation. Then for the K-means clustering algorithm in processing large amounts of data has the disadvantages of high time complexity, This paper uses the K-means algorithm Based on Hadoop platform to realize the clustering analysis of soil fertility of large amounts of data; the results show that: compared with the traditional serial K-means algorithms, improves the operation speed. The above analysis shows that, K- means algorithm is an effective soil fertility evaluation method; Based on Hadoop platform of parallel K-means algorithm has great realistic meaning to analysis of large amount of data of soil fertility factors.
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Guifen Chen, Yuqin Yang, Hongliang Guo, Xionghui Sun, Hang Chen, et al.. Analysis and Research of K-means Algorithm in Soil Fertility Based on Hadoop Platform. 8th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2014, Beijing, China. pp.304-312, ⟨10.1007/978-3-319-19620-6_35⟩. ⟨hal-01420245⟩

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