Abstract:As bank customer segmentation has a profound significance for business marketing, while customer information has the characteristics of large amounts of data high dimensions and frequently-changing demand, we need to introduce a fast algorithm for attribute reduction to meet the needs of rapid attribute extraction to construct decisions. This paper proposes a new quick attribute reduction based on ant colony optimization by improving the collection for each iteration of the ant search transfer strategy. Numerical experiments on a number of UCI datasets show that the proposed new algorithm has a lower computational cost than the traditional ant colony-based attribute reduction algorithm and a better solution quality. Finally, the feasibility of the proposed algorithm is verified through the use of the bank customer data.