User credit profile integrating SOM Neural network and K-means clustering algorithm
To improve the accuracy and real-time performance of user credit profile models based on K-Means clustering algorithm,this paper proposed an improved method that integrated Self Organizing Map(SOM)neural network with K-Means clustering algorithm.The paper used SOM to reduce dimensionality and extract features from user data,directly obtained the optimal number of clusters,and then used K-Means algorithm for clustering analysis,validated the proposed method through real online lending platform data.The results show that the proposed method can improve the quality of user credit profile analysis,better meet the requirements of real-time management and risk control in financial data analysis,and provide accurate decision support for financial institutions.
user credit profileSOM(Self-Organizing Map)neural networkK-Means clustering algorithmtime complexityrisk control
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