Market Basket Analysis for E-Commerce using Association Rule Mining

Authors

  • Kayalvily Tabianan Centre for Emerging Technologies in Computing (CETC), INTI International University, Nilai, Negeri Sembilan, Malaysia
  • Sarasvathi Nagalingham Centre for Emerging Technologies in Computing (CETC), INTI International University, Nilai, Negeri Sembilan, Malaysia
  • Leong Kai Cheng Faculty of Information Technology, INTI International University, Nilai, Negeri Sembilan, Malaysia.

Keywords:

Market Basket Analysis, E-Commerce, KDD methodology

Abstract

Nowadays, shopping with ecommerce has become the most common lifestyle for everyone in modern
era. In order to make the research to be successful, it requires to discover the best research effort to
improve the algorithm. In order to make this research successful, author will need to identify the best
algorithm for finding the item sets frequently bough together and top sales product on each country to
predict the sales. The author has developed an ecommerce system which has back-end system to
display the performance of the product and using Association Rule Mining on the datasets. By using
this system, they can know the hidden product relationships which product has the potential to be
purchase together. For develop the system author has uses KDD research methodology which can help
to extract the minimal support, confidence and lift from the datasets.

Published

2020-09-28

How to Cite

Tabianan, K., Nagalingham, S., & Kai Cheng, L. (2020). Market Basket Analysis for E-Commerce using Association Rule Mining. INTI Journal, 2020. Retrieved from https://iuojs.intimal.edu.my/index.php/intijournal/article/view/205

Issue

Section

Articles