期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：10
页码：311-318
DOI：10.14257/ijhit.2015.8.10.28
出版社：SERSC
摘要：During the iteration process, the traditional K-means algorithm is easily fall into local optimal solution. In order to solve this problem, this paper proposed an improved K- means algorithm, and used the method of maximum distance equal division to select the initial cluster centers. We preset k cluster centers, and avoid it falling into local optimal solution. Apply this improved algorithm into e-commerce customer loyalty analysis, this paper put forward a customer loyalty analysis model using the parameters of shopping recency, shopping frequency, shopping monetary, customer satisfaction and customer attention, and used the improved K-means algorithm to analyze the RFMSA customer loyalty model. The studies show that the improved K-means algorithm and RFMSA model can effectively divide the loyalty of the e-commerce customer, it also can fully reflect the customer's current value and potential value-added ability, and provide the basis that the e-commercial enterprises can adopt different marketing strategies for different target customers.
