The Application of Information Fusion and Extraction in Maize Seed Breeding

It has been the irresistible trend of agriculture informationization which uses information technologies to treat enormous data and finds out potential useful rules to direct the development and reformation of agriculture. Aiming at the specific application of maize seed breeding, this paper effectively integrates several data mining technologies and presents a new method called CA to analyze the whole maize information. The algorithm achieves transverse dimension reduction by combining PCA and other methods, and also achieves longitudinal dimension reduction by improving CURE and k-means. The decision-tree method of CA algorithm introduces three different classifiers in order to enhance the accuracy of trees. By comparing the results of improved algorithm with traditional methods, we can find that the new algorithm is better in performance and degree of parallelism.

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