|Communications on Applied Electronics|
|Foundation of Computer Science (FCS), NY, USA|
|Volume 7 - Number 37|
|Year of Publication: 2021|
|Authors: Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A.|
Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A. . Design of Machine Learning Framework for Products Placement Strategy in Grocery Store. Communications on Applied Electronics. 7, 37 ( Jul 2021), 5-11. DOI=10.5120/cae2021652888
The well-known and most used support-confidence framework for Association rule mining has some drawbacks when employ to generate strong rules; this weakness has led to its poor predictive performances. This framework predicts customers buying behavior based on the assumption of the confidence value, which limits its competent at making a good business decision. This work presents a better Association Rule Mining conceptualized framework for mining previous customers transactions dataset of the grocery store for the optimal prediction of products placed on the shelves, physical shelf arrangement and identification of products that needs promotion. Sampled transaction records were used to demonstrate the proposed framework. The proposed framework leverage the ability of lift metric at improving the predictive performance of Association Rule Mining. The Lift discloses how much better an association rule predicts products to be placed together on the shelf rather than assuming. The proposed conceptualized framework will assist retailers and grocery store owners to easily unlock the latent knowledge or patterns in collected grocery’s store transaction dataset to make important business decisions that will make them competitive and maximize their profit margin.