Study of Mobile Payment User Activity Based on Improved LVQ Clustering
The consumer activity of mobile payment App users is vulnerable to a large number of internal and external factors.In order to improve the classification accuracy of mobile payment App users and analyze the impact factors of each user group's consumer activity,an improved clustering algorithm of learning vector quantization(LVQ)is proposed by analytic hierarchy process(AHP)for feature weighting and optimizing iteration termination conditions based on confidence interval.The im-proved algorithm is applied to divide mobile payment App user groups and build user portraits,and combined with random for-est regression model to analyze impact factors of daily active user(DAU)index to guide the operation and promotion strategy of mobile payment App in the next stage.The experimental results of algorithm performance show that the clustering performance and time performance of the improved algorithm are improved by 22.8 percentage points and 15.5 percentage points respective-ly.
mobile paymentdata miningclusterlearning vector quantizationanalytic hierarchy processconfidence interval
万方数据
维普
