Classifying method of crop data based on fuzzy C mean value utilizing improved gene expression programming

Abstract

The invention discloses a classifying method of crop data based on fuzzy C mean value utilizing improved gene expression programming. The method comprises following steps: using customized similarity to measure a calculating formula and combining with information entropy to calculate weight vector of data attribute; and using a weighed distance calculating formula to replace conventional Euclidean distance and combining a gene expression program with a fuzzy C mean value algorithm to solve a optimal cluster center for classification of crop data. The classifying method of crop data based on fuzzy C mean value utilizing improved gene expression programming has following beneficial effects: on one hand, distance between crop data can be measured so that accuracy of the classification result is increased; and on the other hand, the optimal classification result can be obtained by lower iteration frequency.

Description

technical field [0001] The invention relates to the field of agricultural informatics, in particular to an improved gene expression programming-fuzzy C-mean crop data classification method, which is suitable for the classification or identification of various crop information whose features are described by real numbers. Background technique [0002] The classification or identification of crop data samples is of great significance to crop science research and agricultural production management. Clustering is the main method to achieve classification and identification, and among many clustering algorithms, fuzzy C-means clustering (FCM) "Dunn J C.Afuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[ J].1973,3(3):32-57” is widely used because of its simple algorithm and fast convergence speed, but the Euclidean distance cannot be used to measure the distance of multidimensional data, and it is easy to fall into local extremum, so th...

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Problems solved by technology

However, the literature "Gu Linglan. Effective Clustering Algorithms for Large Datasets [J]. Computer Engineering and Design, 2014, 35(6): 2183-2187." adopts GEP and FCM hybrid algorithm to carry out large data subsets Clustering, but the algorithm does not take into account the repetition and validity of individuals, and does not evaluate the accuracy of the clustering results

Literature "Liu Haitao, Yuan Chang'an, Liu Hailong, et al. Research on fuzzy clustering of remote sensing digital images based on GEP [J]. Computer Engineering, 2010, 36(10): 199-200." Using the global search capability of GEP to conduct FCM Optimization, but the solution to the problem is focused on image processing, and the segmentation effect is not ideal

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