CFP last date
01 May 2024
Call for Paper
June Edition
CAE solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 01 May 2024

Submit your paper
Know more
The week's pick
Responsive image
An Improve Shannon Fano Data Compression Algorithm using Residue Number System

T.D. Lawal L.O. Olatunbosun K.A. Gbolagade
Random Articles
Reseach Article

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

by Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A.
journal cover thumbnail

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.
10.5120/cae2021652888

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

@article{ 10.5120/cae2021652888,
author = { Olasehinde Olayemi O., Abiona Akeem A., Ibiyomi Michael A. },
title = { Design of Machine Learning Framework for Products Placement Strategy in Grocery Store },
journal = { Communications on Applied Electronics },
issue_date = { Jul 2021 },
volume = { 7 },
number = { 37 },
month = { Jul },
year = { 2021 },
issn = { 2394-4714 },
pages = { 5-11 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number37/885-2021652888/ },
doi = { 10.5120/cae2021652888 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:02:56.619735+05:30
%A Olasehinde Olayemi O.
%A Abiona Akeem A.
%A Ibiyomi Michael A.
%T Design of Machine Learning Framework for Products Placement Strategy in Grocery Store
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 37
%P 5-11
%D 2021
%I Foundation of Computer Science (FCS), NY, 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.

References
  1. Hoyer, W.D & Walgren, C.J.C. (1988). Consumer Decision Making Across Product Categories: The Influence of Task Environment. John Wiley & Sons Inc., 5(1) 45-69
  2. Hübner, A. H. and Kuhn, H. (2012). Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega, 40(2):199–209
  3. Dreze, X., Hoch, S. J., & Purk, M. E. (1994). Shelf management and space elasticity. Journal of Retailing, 70, 301–326.
  4. Fancher, L. A. (1991). Computerized space management: A strategic weapon. Discount Merchandiser, 31(3), 64-65.
  5. Hwang, H., Choi, B., & Lee, M. J. (2005). A model for shelf space allocation and inventory control considering location and inventory level effects on demand. International Journal of Production Economics, 97(2), 185-195.
  6. Hübner, A. H. and Schaal, K. (2017). An integrated assortment and shelf-space optimization model with demand substitution and space-elasticity effects. European Journal of Operational Research, 261(1):302–316.
  7. Luís F. M. S. (2018). Optimizing Shelf Space Allocation under Merchandising Rules. A Master’s Dissertation submitted University of Porto.
  8. Olasehinde O. O. (2020). A Stacked Ensemble Intrusion Detection Approach for Security of Information System, International Journal for Information Security Research (IJISR), 10(1).
  9. Olasehinde, O.O., Williams, K.O. and Ajinaja, M.O. (2018): Application of Association Rule Learning in Customer Relationship Management. Proceedings of the 14th iSTEAMS International Multidisciplinary Conference, AlHikmah University, Ilorin, Nigeria, 14: 29-36
  10. Sherdiwala B., Khanna O. (2015). Association Rule Mining: An Overview”, International Multidisciplinary Research Journal (RHIMRJ).
  11. Serban G., Czibula I. G. and Campan A. (2016). A Programming Interface For Medical diagnosis Prediction”,StudiaUniversitatis, "Babes-Bolyai", Informatica, LI(1), pages 21-30, 2006.
  12. Gupta N., Mangal N, Tiwari K. and Mitra P. (200). Mining Quantitative Association Rules in Protein Sequences”, In Proceedings of Australasian Conference on Knowledge Discovery and Data Mining –AUSDM, 273-281, 2000
  13. Raorane A.A., Kulkarni R.V. and Jitkar B.D. (2012). Association Rule – Extracting Knowledge Using Market Basket Analysis, Research Journal of Recent Sciences, 1(2): 19-27.
  14. Liu, H., Su, B., & Zhang, B. (2007). The Application of Association Rules in Retail Marketing Mix. 2007 IEEE International Conference on Automation and Logistics, 2514-2517.
  15. Suchacka, G., Chodak, G. (2017) Using association rules to assess purchase probability in online stores. Inf Syst E-Bus Manage 15, 751–780. https://doi.org/10.1007/s10257-016-0329-4
  16. March N., Reutterer T. (2008) Building an Association Rules Framework for Target Marketing. In: Preisach C., Burkhardt H., Schmidt-Thieme L., Decker R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_52
  17. Phani, P., and Murlidher, M. (2013). A Study on Market Basket Analysis Using a Data Mining Algorithm. International Journal of Emerging Technology and Advanced Engineering, 3(6): 361- 363. Retrieved from www.ijetea.com
  18. Zaki M.J. (2000). Scalable Algorithms for Association Mining. IEEE Transaction on Knowledge and Data Engineering, 12(3): 372-390.
  19. P. Yazgan, A.O. Kusakci. (2016). A Literature Survey on Association Rule Mining Algorithms, Southeast Europe Journal of Soft Computing 5(1), Doi; 10.21533/scjour(nal.v5i1.102
  20. Vidhate, D. (2014). To improve Association Rule Mining using New Technique: Multilevel Relationship Algorithm towards Cooperative Learning 241–246.
  21. Cuzzocrea A., Leung C.K., MacKinnon R.K. (2015). Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data, Procedia Comput. Sci. 613–622.
  22. Naresh P. and Suguna R. (2019). "Association Rule Mining Algorithms on Large and Small Datasets: A Comparative Study," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 587-592, doi: 10.1109/ICCS45141.2019.9065836.
  23. Al-hagery, M.A. (2015). Knowledge Discovery in the Data Sets of Hepatitis Disease for Diagnosis and Prediction to Support and Ser ve Community. Int. J. Comput. Electron. Res. 4, 118–125.
  24. Agrawal, R., Srikant, R. (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB 1994, Santiago de Chile, Chile, (487–499).
  25. Aggarwal, C. C. Data mining: the textbook. Heidelberg: Springer; 2015.
  26. Han, J, Pei, J, Ying, Y, Mao, R. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov., 2004, 8(1):53–87.
  27. Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering. 12 (3), 371- 390. doi:10.1109/69.846291
  28. Pei J., Han, J., Lu H., Nishio S, Tang S, Yang D. (2001) H-mine: Hyper-structure mining of frequent patterns in large databases. In: Proc. 2001 IEEE Intern. Conf. Data Mining, 441–448.
  29. T. Uno, M. Kiyomi, and H. Arimura,(2004) “Lcm ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets,” in Fimi, vol. 126, Proc. ICDM’04 Workshop on Frequent Itemset Mining Implementations, CEUR, 2004.
  30. Houtsma M. and Swami A. (1995) Set-oriented mining of association rules”. Elsevier journal Data & Knowledge Engineering 245-262 Technical Report RJ 9567
  31. Gosain, A., & Bhugra, M. (2013). A comprehensive survey of association rules on quantitative data in data mining. 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, 1003-1008. DOI: https://doi. org/10.1109/cict.2013.6558244.
  32. Fournier-Viger, P., Lin, C., Vo, B., Truong, T.C., Zhang, J., & Le, H. (2017). A survey of itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4) https://doi.org/10.1002/widm.1207
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Market Basket Analysis Frequent Itemset Support Confidence Lift

Powered by PhDFocusTM