Analysis of vegetable purchasing patterns in supermarkets using association rule
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Published:
Nov 21, 2023
Keywords:
Association rule mining, Association analysis, Supermarket, Support with Confidence
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Informtion System
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Abstract
One data mining technique for identifying associative rules between a set of elements is association analysis, often known as association rule mining. Understanding the likelihood that a consumer will purchase bread and milk together is an example of an associative rule from examining purchases made in a supermarket. Supermarket operators can use this information to plan their product placement or create marketing campaigns that use discount coupons for specific product pairings. The use of association analysis to examine the contents of supermarket shopping baskets helped make it well-known. Another name for association analysis is market basket analysis. Perform a multiplication of the numerous rules acquired by Support and Confidence, where the latter should be at least 80%.
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How to Cite
Kasmawaru, K., Djafar, I. ., Rafli R, M. ., Rasyid, R. and Yusuf, M. . (2023) “Analysis of vegetable purchasing patterns in supermarkets using association rule”, Jurnal Mantik, 7(3), pp. 2295-2302. doi: 10.35335/mantik.v7i3.4336.
References
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Supriyamenon, M., & Rajarajeswari, P. (2017). A Review on Association Rule Mining Techniques with Respect to their Privacy Preserving Capabilities. 12(24), 15484–15488.
Terblanche, N. S. (2018). Revisiting the supermarket in-store customer shopping experience. Journal of Retailing and Consumer Services, 40, 48–59.
Weber, F., & Schütte, R. (2019). A domain-oriented analysis of the impact of machine learning—the case of retailing. Big Data and Cognitive Computing, 3(1), 11.
Al-maolegi, M., & Arkok, B. (2014). A N I MPROVED A PRIORI A LGORITHM FOR. 3(1), 21–29.
Antonie, L. (n.d.). I NTRODUCTION TO D ATA M INING WHO AM I??
Apriori, M., & Kunci, K. (n.d.). No Title.
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.
Basuki, A., & Cahyani, A. D. (n.d.). Digital Marketing. Media Nusa Creative (MNC Publishing).
Bharadiya, J. P. (2023). Leveraging machine learning for enhanced business intelligence. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 7(1), 1–19.
Cameron, A. J., Charlton, E., Ngan, W. W., & Sacks, G. (2016). A systematic review of the effectiveness of supermarket-based interventions involving product, promotion, or place on the healthiness of consumer purchases. Current Nutrition Reports, 5, 129–138.
Chen, L. (2012). The Research of Data Mining Algorithm Based on Association Rules. 548–551.
Deshpande, S. P., & Thakare, V. M. (2010). DATA MINING SYSTEM AND APPLICATIONS?: A REVIEW. 1(1), 32–44.
Gomez, M. I., McLaughlin, E. W., & Wittink, D. R. (2004). Customer satisfaction and retail sales performance: an empirical investigation. Journal of Retailing, 80(4), 265–278.
Gufroni, A. I. (2018). Associative Analysis Data Mining Pattern Against Traffic Accidents Using Apriori Algorithm. 5(2), 91–104.
Heller, A. (2006). Consumer-centric category management: How to increase profits by managing categories based on consumer needs. Wiley Online Library.
Hokka, J. (2023). Controlled Experiments for Data-driven Retail Optimization.
Husain, T., & Hidayati, N. (2021). THE OPTIMIZE OF ASSOCIATION RULE METHOD FOR THE BEST BOOK PLACEMENT PATTERNS IN LIBRARY?: A MONTHLY TRIAL. 4(2), 53–59. https://doi.org/10.31943/teknokom.v4i2.63
Jiménez-Guerrero, J. F., Pérez-Mesa, J. C., de Burgos-Jiménez, J., & Piedra-Munoz, L. (2018). Considering the consumer in the design of a supply chain of perishables. International Food and Agribusiness Management Review, 21(4), 525–542.
Kasieczka, G., Nachman, B., Huang, Z., Cha, Y., Seo, J., & Kim, J. (2021). Data Mining preparation?: Process , Techniques and Major Issues in Data Analysis Data Mining preparation?: Process , Techniques and Major Issues in Data Analysis. https://doi.org/10.1088/1757-899X/1090/1/012053
Kaur, J., & Madan, N. (2015). Association Rule Mining?: A Survey. 8(7), 239–242.
Kumar, S. (2008). A study of the supermarket industry and its growing logistics capabilities. International Journal of Retail & Distribution Management, 36(3), 192–211.
Liao, S., Chu, P., & Hsiao, P. (2012). Expert Systems with Applications Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems With Applications, 39(12), 11303–11311. https://doi.org/10.1016/j.eswa.2012.02.063
Meliza, J., & Lubis, K. S. (2022). Manajemen Pemasaran dan Strateginya. Penerbit P4I.
Mishra, S., & Tyagi, A. K. (2022). The role of machine learning techniques in internet of things-based cloud applications. Artificial Intelligence-Based Internet of Things Systems, 105–135.
Niemeier, S., Zocchi, A., & Catena, M. (2013). Reshaping retail: Why technology is transforming the industry and how to win in the new consumer driven world. John Wiley & Sons.
Pereira, M. M., & Frazzon, E. M. (2021). A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. International Journal of Information Management, 57, 102165.
Purohit, S., & Jain, A. K. (2020). Evolution of Fuel Retail in India Vis-a-vis Indian Customer: Shift in Consumer Behavior. International Journal of Management, 11(8).
Ramageri, M. (n.d.). DATA MINING TECHNIQUES AND APPLICATIONS. 1(4), 301–305.
Sarno, R., Dewandono, R. D., Ahmad, T., & Naufal, M. F. (2015). Hybrid Association Rule Learning and Process Mining for Fraud Detection. April.
Siekmann, J., Hartmanis, J., & Leeuwen, J. Van. (n.d.). Lecture Notes in Artificial Intelligence.
Sousa, L. R. De, Carvalho, V. O. De, Penteado, B. E., & Jos, F. (2021). A Systematic Mapping on the Use of Data Mining for the Face-to-Face School Dropout Problem. 1(Csedu), 978–989. https://doi.org/10.5220/0010476300360047
Supriyamenon, M., & Rajarajeswari, P. (2017). A Review on Association Rule Mining Techniques with Respect to their Privacy Preserving Capabilities. 12(24), 15484–15488.
Terblanche, N. S. (2018). Revisiting the supermarket in-store customer shopping experience. Journal of Retailing and Consumer Services, 40, 48–59.
Weber, F., & Schütte, R. (2019). A domain-oriented analysis of the impact of machine learning—the case of retailing. Big Data and Cognitive Computing, 3(1), 11.
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