A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Tree

ISSN Print: 2164-5167
ISSN Online: 2164-5175
www.scirp.net/journal/ajibm
E-mail: [email protected]

Citations

"A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Tree"
written by Haiying Ma,
published by American Journal of Industrial and Business Management, Vol.5 No.12, 2015

has been cited by the following article(s):

[1]	Linear Regression vs. Support Vector Machine: A Formal Enhancement in Online Purchase utilizing Novel Customer Feedback Segmentation
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[2]	Understanding Customer Purchasing Decisions using RFM and Machine Learning
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[3]	Customer Segmentation Techniques on E-Commerce
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[4]	Application of Clustering Algorithm for Effective Customer Segmentation in E-Commerce
	2021

[5]	Design of an Intelligent Customer Identification Model in e-Commerce Logistics Industry
	2019

[1]	Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering Lecture Notes in Electrical Engineering, 2026 DOI:10.1007/978-981-95-0269-1_75

[2]	Application of Clustering Algorithm for Effective Customer Segmentation in E-Commerce 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021 DOI:10.1109/ICCIKE51210.2021.9410713

[3]	Customer Segmentation Techniques on E-Commerce 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2021 DOI:10.1109/ICACITE51222.2021.9404659

[4]	Design of an Intelligent Customer Identification Model in e- Commerce Logistics Industry MATEC Web of Conferences, 2019 DOI:10.1051/matecconf/201925504003