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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Science, Engineering, Technology, and Education JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology
Ojugo, Arnold Adimabua
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Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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CLUSTER PREDICTION MODEL FOR MARKET BASKET ANALYSIS: QUEST FOR BETTER ALTERNATIVES TO ASSOCIATIVE RULE MINING APPROACH Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i3.pp%p

Abstract

Market basket analysis seeks to apply association rule mining on the massive sales transaction data. It yields an outcome that either aims to suppress product stock-up unnecessarily and/or product being stock-out. Such decision support system seeks to avoid the unnecessary demurrage and help businesses to keep their customers via better decision and improved service. Market data are time-bound on supply-demand value chain. With customer behavior varying in time, we seek to predict purchase of commonly combined itemset for a next period ? so that businesses can better support their decisions via adequate provisions of the required inventory. We use 3-KDD dataset and Delta Mall dataset ? adapting a time-clustering algorithm that examines buying behavior of customers, their preferences and frequency with which goods are purchased in common as a basket. Model yields average 162-rules for four-dataset from dataset. Result shows that previous basket items by random customers allow the selection purchase of items of similar value as best combined due to its shelf-placement using the concept of feature drift.
Co-Authors Eboka, Andrew Okonji Nwankwo, Obinna Obruche, Chris Obaro Otakore, Oghenevwede Debby Yoro, Rume Elizabeth