Abstract
Fuzzy value is much important in credit risk management of commercial banks. However, there is not a perfect method of cluster analysis to analyze the data. The traditional clustering method is not suitable for fuzzy value clustering. Although lattice clustering uses lattice degree instead of Euclidean metric to calculate distance, it also has some problems, such as obscure concept of grid progress, complex calculation process, long calculation time, and so on. Therefore, the Word2vec model is introduced to convert the fuzzy value into a probability matrix that can be measured by Euclidean distance, then we analyze the probability matrix clustering. From the experiments, we can find Word2vec is much better for fuzzy value.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (Grant No. 72101279).
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Wang, J., Lin, J., Han, L. (2023). Word2vec Fuzzy Clustering Algorithm and Its Application in Credit Evaluation. In: Wang, T., Patnaik, S., Ho Jack, W.C., Rocha Varela, M.L. (eds) Applications of Decision Science in Management. ICDSM 2022. Smart Innovation, Systems and Technologies, vol 260. Springer, Singapore. https://doi.org/10.1007/978-981-19-2768-3_56
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DOI: https://doi.org/10.1007/978-981-19-2768-3_56
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