Multiple Association Rule Mining Algorithms for Supply Chain Finance Risk Prediction

DOI

10.2991/978-94-6463-198-2_92How to use a DOI?

Keywords

supply chain; financial risk; Apriori association method

Abstract

As China’s economy enters a new stage of development, the financial dilemmas faced by small and medium-sized enterprises, which are a major component of the national economy, are attracting more and more attention. In this paper, while reviewing a large amount of literature, the causes and correlations of supply chain financial risks are analyzed in a relevant way, and the corresponding data are generated by data mining, and the Apriori correlation algorithm and its related improvement algorithm are examined so as to obtain the relationships between risks, and corresponding risk prevention countermeasures are given based on the results of data mining.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuezheng Yang
PY  - 2023
DA  - 2023/08/10
TI  - Multiple Association Rule Mining Algorithms for Supply Chain Finance Risk Prediction
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 888
EP  - 893
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_92
DO  - 10.2991/978-94-6463-198-2_92
ID  - Yang2023
ER  -