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 Kmeans 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.
목차
Abstract 1. Introduction 2. Build RFMSA Customer Loyalty Model 3. Improved K-means Algorithm 3.1 K-means Algorithm 3.2 The Improved Selection Method of Initial Cluster Center 4. Algorithm Implementation 4.1 The Customer Loyalty Analysis of Improved K-means Algorithm 4.2 Weighted Analysis of RFMSA 4.3 The Pre-processing of Experiment Data 4.4 Clustering Result and Strategy Analysis 5. Conclusion ACKNOWLEDGEMENTS References
보안공학연구지원센터(IJHIT) [Science & Engineering Research Support Center, Republic of Korea(IJHIT)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Hybrid Information Technology
간기
격월간
pISSN
1738-9968
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Hybrid Information Technology Vol.8 No.10