Application of Improved Canopy Algorithm in Customer Clustering

In order to improve the accuracy and efficiency of customer clustering, an improved Canopy algorithm is proposed. The initial cluster centers are generated based on the principle of maximum and minimum by using Canopy algorithm. Then the overlapping data is classified by shared nearest neighbor algorithm. For different sizes of order data sets, the comparison between the proposed algorithm and the SNN algorithm in terms of the actual effect is conducted. The experimental results show that the proposed method effectively solves the problem of processing overlapping data of traditional Canopy algorithm and the redundant distance calculation in iterative is avoided. The improved algorithm has better performance than the traditional algorithm. The algorithm effectively improves analysis time and has great application value in the analysis of bank data and forecasting customer behavior. The clustering results can provide support for the bank's sales behavior.