Abstract
With the progresses of meteorological informatization, large amounts of meteorological data were accumulated. For the better application of these data, this research introduced data mining and applied it to analysis meteorological disaster, which can extract implicit, and potential useful information from a large amount of data in order to guide agricultural production. Meteorological disaster data affecting tea production were analyzed based on Apriori algorithm in this research, and the strong association rules between meteorological factors and reduction rate of tea yield were found. The results showed that the reduction rate of tea yield is closely related to the daily minimum temperature in the germination stage. When the daily minimum temperature was less than 5 °C, the tea would be affected by the cold and freezing damage and the tea yield would decrease, the lower the temperature, the greater the cold and freezing damage.
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References
Tao Guo, F., Daiyuan Zhang, S.: Research and application on association rules based on apriori algorithm. Comput. Technol. Dev. 21(6), 102–107 (2011)
Yan Cui, F., Zhiqiang Bao, S.: Survey of association rule mining. Appl. Res. Comput. 33(2), 330–334 (2016)
Bin Sun, F., Yanling Han, S., Guochao Feng, T.: Application of improved apriori algorithm in studying factors of soil reflectivity. Comput. Appl. Softw. 34(7), 293–297 (2017)
Yuntao Zhang, F., Ling Gong, S.: The Principle and Technology of Data Mining, 1st edn. Publishing House of Electronics Industry, Beijing (2004)
Jiawei Han, F., Micheline Kamber, S., Jian Pei, T.: Data Mining Concepts and Techniques, 3rd edn. China Machine Press, Beijing (2012)
Yanqun Wang, F., Xianqiang Gao, S.: Application of apriori algorithm of association rules in information mining of corn yield. Hubei Agric. Sci. 55(3), 736–739 (2016)
Wenjuan Qi, F., Jie Yan, S.: Association rules apriori algorithm in data mining. Comput. Syst. Appl. 22(4), 121–124 (2013)
Qiao Li, F., Chunhua Yang, S.: Application of association rules apriori algorithm in teaching evaluation. Comput. Dig. Eng. 81(6), 49–51 (2010)
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Wang, C., Zhao, B. (2018). Application of Apriori Algorithm in Meteorological Disaster Information Mining. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2018. Lecture Notes in Computer Science(), vol 11151. Springer, Cham. https://doi.org/10.1007/978-3-030-00560-3_35
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DOI: https://doi.org/10.1007/978-3-030-00560-3_35
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