A Retail Outlet Classification Model Based on AdaBoost
- OAPS－大学生创新计划项目 
This paper proposes a framework to get a stable classification rule under unsupervised learning, and the term ‘‘stable’’ means that the rule remains unchanged when the sample set increases. This framework initially makes use of clustering analysis and then use the result of clustering analysis as a referencestudying sample. Secondly, AdaBoost integrated several classification methods is used to classify the samples and get a stable classification rule. To prove the method feasible, this paper shows an empirical study of classifying retail outlets of a tobacco market in a city of China. In this practice, k-means is used to make clustering analysis, and AdaBoost integrated RBF neural network, CART, and SVM is used in classification. In the empirical study, this method successfully divides retail outlets into different classes based on the sales ability.