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- W4361218392 abstract "Abstract In China, post loan management is usually executed in the form of visit survey by credit man. Through quarterly visit survey, a large number of loan audit short texts are collected, which contain valuable information for evaluating the credit status small and micro enterprises. However, there is still lack of methods for analyzing this kind of short texts. This paper proposes a method for processing these loan audit short texts called Fuzzy Clustering Analysis (FCA). This method firstly transforms short texts into a fuzzy matrix through lexical analysis; Then, the similarity between records is calculated based on each fuzzy matrix, and an association graph is constructed with the similarity. Finally, Prim minimum spanning tree is used to extract clusters based on different α cuts. Experiments with actual data from a commercial bank in China have revealed that FCA yields suitable clustering results when handling loan audit briefs. Moreover, it exhibits superior performance compared to BRICH, Kmean, and FCM.." @default.
- W4361218392 created "2023-03-31" @default.
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- W4361218392 date "2023-03-28" @default.
- W4361218392 modified "2023-09-23" @default.
- W4361218392 title "Fuzzy clustering analysis for the loan audit short texts" @default.
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- W4361218392 doi "https://doi.org/10.21203/rs.3.rs-2734237/v1" @default.
- W4361218392 hasPublicationYear "2023" @default.
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