Abstract
Facing the cruel market competition environment, the enterprise’s demand for risk management is increasing day by day. How to objectively evaluate the financial risks existing in the process of enterprise management and how to timely warn them is the goal that the enterprise management always pursues. Enterprise financial risk analysis and early warning research is influenced by various factors inside and outside the enterprise. The results show that the uncertainty of the technology is very high, and the excellent performance of data mining technology in the study of uncertainty theory links the two closely. In addition, it also proves that the three improved algorithms of association rules greatly improve the efficiency of data mining. Meanwhile, the concept hierarchy tree model of enterprise financial risk and the financial crisis early-warning model of dynamic maintenance of time series are put forward. In conclusion, these algorithms are suitable for the research of enterprise financial risk analysis and crisis warning.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 71001075), and the Fundamental Research Funds for the Central Universities (Grant Nos. skqy201739, skqy201409 and skzx2017-sb35).
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Jin, M., Wang, Y. & Zeng, Y. Application of Data Mining Technology in Financial Risk Analysis. Wireless Pers Commun 102, 3699–3713 (2018). https://doi.org/10.1007/s11277-018-5402-5
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DOI: https://doi.org/10.1007/s11277-018-5402-5