Research of Large-Scale and Complex Agricultural Data Classification Algorithms Based on the Spatial Variability
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
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stract. 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. Keywords: Large-scale and complex data Spatial variation law clustering Soil nutrients Sensitive attribute weights
Fuzzy
1 Introduction The arrival of the era of precision agriculture [1, 2], makes a variety of complex link relationship between agricultural data features with apparent spatial variability [3] and the correlation. The consequent massive, diverse and dynamic changes, incomplete, uncertain and a series of features, so that each attribute internal link close, but contact between attributes relatively sparse [4]. However, data mining can effectively for data analysis, Wherein the cluster analysis can be used as an independent tool to obtain data distribution situation, so that can observe characteristics of each class, analysis some specific class to move forward a single step, Final extract useful information. But with the rising importance of data structure information and the data on the exponential growth. This shows traditional data mining algorithms have been unable to meet these needs. How to © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved D. Li and Z. Li (Eds.): CCTA 2015, Part I, IFIP AICT 478, pp. 45–52, 2016. DOI: 10.1007/978-3-319-48357-3_5
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introduce spatial patterns of in large-scale agriculture data [5]. And to strengthen the links between attributes for regional management in order to improve the parallel and distributed implementation strategy of clustering algorithm [6, 7]. All of these are gradually attracted researchers’ attention [8]. So, on the basis of K-means algorithm, according to the interdependence of spatial unit location. Li [9], who put forward a new Spatial Contiguous K-Means Cluster algorithm, who removed a lot of debris and isolated cell and taken into account the continuity of the management partition. The actual show
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