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Design of Machine Learning Framework for Products Placement Strategy in Grocery Store

Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A.. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2021
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A.
10.5120/cae2021652888

Olasehinde Olayemi O., Abiona Akeem A. and Ibiyomi Michael A.. Design of Machine Learning Framework for Products Placement Strategy in Grocery Store. Communications on Applied Electronics 7(37):5-11, July 2021. BibTeX

@article{10.5120/cae2021652888,
	author = {Olasehinde Olayemi O. and Abiona Akeem A. and Ibiyomi Michael A.},
	title = {Design of Machine Learning Framework for Products Placement Strategy in Grocery Store},
	journal = {Communications on Applied Electronics},
	issue_date = {July 2021},
	volume = {7},
	number = {37},
	month = {Jul},
	year = {2021},
	issn = {2394-4714},
	pages = {5-11},
	numpages = {7},
	url = {http://www.caeaccess.org/archives/volume7/number37/885-2021652888},
	doi = {10.5120/cae2021652888},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

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Keywords

Association Rule Mining, Market Basket Analysis, Frequent Itemset, Support, Confidence, Lift