关键词: 改进轮廓系数, 最佳k值, 客户分群, 特征选择, 用户画像

Abstract: In view of the fact that the airlines evaluate the clustering effect in the clustering of customers and determine the best k-valued contour coefficient method, the time complexity is too high (O2) and the accuracy is low, firstly, the paper proposes an improved contour coefficient method, which uses the distance calculation between objects and clusters or different cluster centers to replace the distance calculation between similar or heterogeneous objects, and determines the contour coefficient adjustment position and weight adjustment by the clustering effect change rate; secondly, the paper builds a clustering model based on real-time airline customer data after pre-processing and feature selection, and determines the optimal customer grouping by means of improved contour coefficient method, and then constructs user profiles; finally, the paper describes the characteristics of different airline group customers and proposes corresponding personalized service measures to assist airlines to provide personalized products and services to customers. The results show that the accuracy of the improved contour coefficient method under different sample sizes is significantly improved and the operating efficiency is significantly improved and the airline customer grouping results, which are based on the improved contour coefficient method, are in line with objective reality, and the proposed service measures can provide reference for airlines to maximize customer demands and increase customer satisfaction.

Key words: improved silhouette coefficient, the optimal k value, customer clustering, feature selection, user profile