Fuzzy clustering of clients' credit risk for futures company

摘要

Based on the real clients' transaction data, several characteristic indices are defined and computed first. These indices then serve as basic variables for clustering. K-means clustering and improved fuzzy clustering approaches are applied to client classification. The final classification is obtained by using intersection-based clustering combination algorithm. The clustering result provides the scientific base for futures companies to improve the clients' risk management.