Construction and Analysis of Macroeconomic Forecasting Model Based on Biclustering Algorithm

Chao Pang and Miaochao Chen

Journal of Mathematics, 2022, vol. 2022, 1-10

Abstract: In recent years, with the globalization of information and Internetization, the phenomenon of information overload has appeared in the production of large amounts of data, and data mining has emerged as the times require. Clustering technology is a representative data mining technology. Cluster analysis has been applied to data mining and has achieved significant results. However, with the deepening of people’s understanding, it has been found that this either/or classification is increasingly not suitable for fuzzy classification problems. Therefore, fuzzy clustering technology, which combines the strengths of machine learning and fuzzy mathematics, has become the new darling of clustering technology and has achieved outstanding results in clustering accuracy. How to obtain a more accurate division from the vast economic statistics of the Statistical Yearbook has become a difficult problem, especially when there is no prior information. Based on China’s macroeconomic statistics, this paper applies the biclustering method to the field of economic zoning for the first time, researches and predicts the economic region division plan of China’s provinces and the economic growth model of each province, and combines the results with traditional levels. The results of the class methods are compared. The research results show that the hierarchical clustering algorithm is relatively intuitive and easy to apply to the overall analysis of the national economic divisions. The result of the biclustering algorithm has its unique advantages in mining the commonalities of various provinces under certain attribute sets.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:7768949

DOI: 10.1155/2022/7768949

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