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Research of Large-Scale and Complex Agricultural Data Classification Algorithms Based on the Spatial Variability

Abstract : In the actual classification problems, as a result of lack of clear boundary information between classification objects, that could lead to loss of classification accuracy easily. Therefore, this article from the spatial patterns of the sample properties to proceed, fuzzy clustering algorithm is proposed based on the sensitivity of attribute weights, through using the attribute weights to improve the classification capability between confusing samples, that is for researching and analysing soil nutrient spatial data with consecutive years to collect in Nongan town. Then through the analysis of the visualization technology to realize the visualization of the algorithm. Experimental results show that introducing weights portray attribute information could reduce the objective function value, and effectively alleviate the phenomenon of boundary data that cannot distinguish. Ultimately to improve the classification accuracy. Meanwhile, use of MATLAB to form visualization of three-dimensional image. The results provide a basis for to improve the accuracy of data classification and clustering analysis of large and complex agricultural data.
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Hang Chen, Guifen Chen, Lixia Cai, Yuqin Yang. Research of Large-Scale and Complex Agricultural Data Classification Algorithms Based on the Spatial Variability. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.45-52, ⟨10.1007/978-3-319-48357-3_5⟩. ⟨hal-01557815⟩



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