Abstract
In order to help decision-making departments to know the information of regional economic development in time, the economic prediction and early warning model and economic policy simulation model of the decision-making system are constructed by analyzing the influencing factors of regional economy and taking the fuzzy C-means clustering as the main algorithm. Based on the data in the statistical yearbook, the corresponding module functions of the system are completed. Then, the system is applied to predict the recent economic development indicators. The results show that the forecasting data and the real data of the indicators are on the same level as a whole. When the growth rate of foreign trade export, fixed assets investment, fiscal expenditure and total retail sales of social consumer goods are increased by 1 percentage point, respectively, the GDP growth rate will increase by 0.105, 0.113, 0.134, 0.087 and 0.075 percentage points. The research suggests that the support system basically meets the requirements, can achieve the purpose of prediction, and has certain practicability.
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Ahmad S, Simonovic SP (2006) An intelligent decision support system for management of floods. Water Resour Manag 20(3):391–410
Allen PG, Morzuch BJ (2006) Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years? Int J Forecast 22(3):475–492
Ghodsypour SH, O’Brien C (1998) A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. Int J Prod Econ 56–57(1–4):199–212
Hernes M, Sobieska-Karpińska J (2016) Application of the consensus method in a multiagent financial decision support system. Inf Syst e-Bus Manag 14(1):167–185
Javid J, Mohammad F (2016) Hybrid electromagnetism-like algorithm for dynamic supply chain network design under traffic congestion and uncertainty. Math Probl Eng 2016:1–18
Keen PGW (1985) Computers and managerial choice. Organ Dyn 14(2):35–49
Kesemen O, Tezel Ö, Özkul E (2016) Fuzzy c-means clustering algorithm for directional data (FCM4DD). Exp Syst Appl 58(C):76–82
Lin CW, Yang L, Fournier-Viger P et al (2017) A binary PSO approach to mine high-utility itemsets. Soft Comput 21(17):5103–5121
Liu L, Sun SZ, Yu H et al (2016) A modified fuzzy C-means clustering algorithm and its application on carbonate fluid identification. J Appl Geophys 129:28–35
Macqueen J, Redheffer RM (1965) Some applications of monotone operators in Markov processes. Ann Math Stat 36(5):1421–1425
Maihami R, Karimi B (2017) Effect of two-echelon trade credit on pricing-inventory policy of non-instantaneous deteriorating products with probabilistic demand and deterioration functions. Ann Oper Res 257(1):1–37
Markin MV (2018) On the Carleman classes of vectors of a scalar type spectral operator. Int J Math Math Sci 2004(60):3219–3235
Pai PF, Chen LC, Lin KP (2016) A hybrid data mining model in analyzing corporate social responsibility. Neural Comput Appl 27(3):749–760
Pradhananga R, Taniguchi E, Yamada T et al (2016) Risk of traffic incident delay in routing and scheduling of hazardous materials. Int J Intell Transp Syst Res 14(1):50–63
Rama D, Andrews JD (2016) Railway infrastructure asset management: the whole-system life cost analysis. IET Intell Transp Syst 10(1):58–64
Roni MS, Eksioglu SD, Cafferty KG et al (2016) A multi-objective, hub-and-spoke model to design and manage biofuel supply chains. Ann Oper Res 249(1–2):1–30
Vagin VN, Morosin OL, Fomina MV (2016) Inductive inference and argumentation methods in modern intelligent decision support systems. J Comput Syst Sci Int 55(1):79–95
Wojtkowski M, Srinivasan V, Ko T et al (2004) Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation. Opt Exp 12(11):2404–2422
Xiao M, Xiao Z, Wen Z et al (2017) Improved FCM clustering algorithm based on spatial correlation and membership smoothing. J Electron Inf Technol 39(5):1123–1129
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhang T, Chen L, Chen CLP (2016) Clustering algorithm based on spatial shadowed fuzzy C-means and I-Ching operators. Int J Fuzzy Syst 18(4):609–617
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Wang, J., Yu, C. & Zhang, J. Constructing the regional intelligent economic decision support system based on fuzzy C-mean clustering algorithm. Soft Comput 24, 7989–7997 (2020). https://doi.org/10.1007/s00500-019-04091-3
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DOI: https://doi.org/10.1007/s00500-019-04091-3