南京理工大学学报（自然科学版）Issue(4)：531-536,6.
基于改进K-means算法的RFAT客户细分研究
RFAT customer segmentation based on improved K-means algorithm
摘要
Abstract
The traditional K-means algorithm has sensitivity to the initial cluster centers,meanwhile it is difficult for users to determine the optimal number of clusters in advance. In order to solve these problems,a new improved K-means algorithm is proposed here. The algorithm can optimize the initial center points through computing the maximum distance of objects. At the same time,it can find the optimal number of clusters by using a new evaluation function. The results can reduce the dependence on the parameters. When the improved algorithm is used to analyze customers of a firm, the RFAT customer classification model is proposed. The new model has four segmentation variables to assess the customer’s value:Recency, Frequency, Average Monetary and Trend. The customers RFAT-value is analyzed by using clustering. The business strategy for different customer groups is also pointed out. The application results show that the RFAT model and the improved K-means algorithm proposed here can classify customers effectively. It also can fully reflect the customer’s current value and appreciation potential.关键词
客户分类/购买时间/购买频次/平均购买额/购买倾向/K-means算法/初始聚类中心/聚类数Key words
customer classification/recency/frequency/average monetary/trentd/K-means algorithm/initial clustering centers/number of clusters分类
信息技术与安全科学引用本文复制引用
刘芝怡,陈功..基于改进K-means算法的RFAT客户细分研究[J].南京理工大学学报（自然科学版）,2014,(4):531-536,6.基金项目
江苏省自然科学基金(BK20130245) （BK20130245）
