Abstract
Data visualization is an important method in data mining, and temperature data map is one of the methods of data visualization. When sample temperature points are limited, it brings the main content of the research on how to build a temperature data map, namely how to use super-sparse date to generate a data map. Based on the fact that temperature energy keeps constant, this paper proposes a diffusion model which uses the known temperature data to predict the unknown values. Furthermore, this paper improves the model by adding the influence of wind to make a better temperature data map. Taking the area of Shandong Province as an example, we can only get 17 cities’ annual average temperature, then how to make a whole map of Shandong area confronts us. Experimental results verified that the proposed diffusion models turn out to be feasible.
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Fu, D., Li, W., Li, X., Wang, X. (2013). Research on a Super-Sparse Data Generation Model for Temperature Data Map. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_34
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DOI: https://doi.org/10.1007/978-3-642-42057-3_34
Publisher Name: Springer, Berlin, Heidelberg
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