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Issue title: Special Section: Intelligent & fuzzy theory in engineering and science
Guest editors: Teresa Guarda, Isabel Lopes and Álvaro Rocha
Article type: Research Article
Affiliations: Department of Economics and Management, Shandong Yingcai University, Jinan, Shandong, China
Correspondence: [*] Corresponding author. Lu Yao, Department of Economics and Management, Shandong Yingcai University, Jinan, Shandong, 250000, China. E-mail: aidisin@126.com.
Abstract: With the introduction of the information age, enterprise financial management has been challenged as never before, and the application of Internet of Things (IoT) technology can effectively improve the efficiency of financial accounting management and realize the informationization of financial management. In order to solve the problem of enterprise financial accounting data processing, a data mining algorithm is constructed, which uses data mining technology to obtain massive information data and cluster analysis processing to realize the fusion of multiple uncertainty information processing models. Firstly, the financial information cloud platform is designed by using the IoT technology. The financial risk index coefficient of the enterprise is judged by the association rules. Finally, the research sample is divided into the risk group and the normal group according to the ST classification standard, and the 296 financial indicators of the two groups are correlated. The research results show that if the enterprise with a score below 40 points has financial risk, the accuracy rate is 70.9%, which is slightly lower than the financial risk warning model of the decision tree. Through the research of this paper, it has enlightenment to the financial accounting management of IoT enterprises. The data mining technology is applied in the processing of massive data information of accounting, which is moreefficient.
Keywords: data mining algorithm, enterprise, financial accounting, smart management
DOI: 10.3233/JIFS-179173
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 5915-5923, 2019
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