Keywords
How to Cite
Abstract
Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.
References
Grewal, Dhruv, Michael Levy, and Vijay Kumar, "Customer experience management in retailing: An organizing framework," Journal of Retailing, vol. 85, no. 1, pp. 1--14, 2009.
Wang, Zhanpeng, et al, "Optimal retail sales strategies for old and new products in monopoly and horizontal competition scenarios," Journal of Retailing and Consumer Services, vol. 71, p. 103218, 2023.
Nouinou, Hajar, et al., "Decision-making in the context of Industry 4.0: Evidence from the textile and clothing industry," Journal of Cleaner Production, p. 136184, 2023.
Blattberg, Robert C., et al., "Market basket analysis," Database Marketing: Analyzing and Managing Customers, pp. 339--351, 2008.
Raorane, Abhijit, and R. V. Kulkarni., "Data mining techniques: A source for consumer behavior analysis," arXiv preprint arXiv:1109.1202, 2011.
Bihl, Trevor, et al., "Artificial Neural Networks and Data Science," Encyclopedia of Data Science and Machine Learning, pp. 899--921, 2023.
Sharma, Moolchand, et al., "Intelligent Data Analysis for Medical Applications," Intelligent Data Analysis: From Data Gathering to Data Comprehension, pp. 333--346, 2020.
Sarhan, Amany M., "Data Mining in the Internet of Things Systems: A Literature Review,"
Journal of Engineering Research, vol. 6, no. 5, pp. 252--263, 2023.
Solanke, Abiodun Abdullahi. "Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques." (2022).
Kaur, Manpreet, and Shivani Kang. "Market Basket Analysis: Identify the changing trends of market data using association rule mining." Procedia computer science 85 (2016): 78-85.
Kaur, Manpreet, and Shivani Kang. "Market Basket Analysis: Identify the changing trends of market data using association rule mining." Procedia computer science 85 (2016): 78-85.
Kaur, Manpreet, and Shivani Kang. "Market Basket Analysis: Identify the changing trends of market data using association rule mining." Procedia computer science 85 (2016): 78-85.
Patel, Suresh B., Sanjay M. Shah, and Mahendra N. Patel. "An Efficient Search Space Exploration Technique for High Utility Itemset Mining." Procedia Computer Science 218 (2023): 937-948.
H. H. Nasereddin, "New Data Mining Techniques to deal with both Static and dynamic
databases," 2007.
Joshi, ASHISH P., and BIRAJ V. Patel "Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process," Orient. J. Comput. Sci. Technol, vol. 13, no. 0203, pp. 78--81, 2021.
Guha, Abhijit, et al. "How artificial intelligence will affect the future of retailing." Journal of Retailing 97.1 (2021): 28-41.
Rehman, Iqra, and Hamid Ghous. "Structured Critical Review on Market Basket Analysis using Deep Learning & Association Rules." International Journal of Scientific & Engineering Research 12.1 (2021): 1-24.
SEYMEN, Omer Faruk, et al. "Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment." Gazi University Journal of Science (2023).
Alzubaidi, Laith, et al. "Review of deep learning: Concepts, CNN architectures,
challenges, applications, future directions." Journal of big Data 8 (2021): 1-74.
Rita, Paulo, Tiago Oliveira, and Almira Farisa. "The impact of e-service quality and customer satisfaction on customer behavior in online shopping." Heliyon 5.10 (2019).
D. a. O. K. a. O. B. a. M. A. Y. a. O. T. Alcan, "A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining," in Industrial Engineering in the Covid-19 Era: Selected Papers from the Hybrid Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2022, October 29-30, 2022, Springer, 2023, pp. 128--137.
L.-C. Ma, "Novel next-group recommendation approach based on sequential market
basket information," Electronic Commerce Research, pp. 1--20, 2022.
S. a. R. A. a. K. R. a. N. N. Karnila, "MARKET BASKET ANALYSIS ON TRANSACTION DATA USING THE APRIORI ALGORITHM," Jurnal TAM (Technology Acceptance Model), vol. 13, no. 1, pp. 34--43, 2022.
G. M. I. a. R. X. D. M. a. L. Y. J. G. a. G. J. A. T. Caldas, "A Product Network Analysis Using A Priori Algorithm for Extending the Market Basket in Retail," in Proceedings of the First Australian International Conference on Industrial Engineering and Operations, Sydney, Australia, 2022.
M. R. a. S. M. a. I. H. a. C. J. C. a. S. A. Pradana, "Market Basket Analysis Using FP-Growth Algorithm On Retail Sales Data," in 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), IEEE, 2022, pp. 86-
-89.
A. a. P. S. Tripathi, "Market Basket Analysis of Cosmetic Products using Apriori Algorithm," Journal) Volume, vol. 2, no. 1, 2022.
Y. A. Unvan, "Market basket analysis with association rules," Communications in Statistics-Theory and Methods, vol. 50, no. 7, pp. 1615--1628, 2021.
S. C. a. M. K. Bilqisth, "Determination of Temporal Association Rules Pattern Using Apriori Algorithm," IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 2, pp. 159--168, 2020.
Z. a. T. K. D. NURZANI, "Analysis of Transactions 212 Mart Kuto Palembang to Find Frequent Patterns Among Itemset Using Association Rule Mining," in Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), Atlantis Press, 2020, pp. 325--332.
P. a. H. K. A. a. G.-H. E. a. Z. H. a. F.-F. M. Ebrahimi, "Consumer knowledge sharing behavior and consumer purchase behavior: Evidence from E-commerce and online retail in Hungary," Sustainability, vol. 13, no. 10, p. 10375, 2021.
D. a. S. I. Gal, "redicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not," Consumer Psychology Review, vol. 4, no. 1, pp. 135--152, 2021.
N. K. a. S. K. Chauhan, "A review on conventional machine learning vs deep learning," in 2018 International conference on computing, power and communication technologies (GUCON), IEEE, 2018, pp. 347--352.
S. C. a. L. J. a. Y. N. Chintala, "Browsing the Aisles or Browsing the App? How Online Grocery Shopping Is Changing What We Buy," How Online Grocery Shopping is Changing What We Buy (August 8, 2022), 2022.
M. a. B. M. a. E.-g. S. a. A. R. a. E.-d. A. Shawkat, "An optimized FP-growth algorithm for discovery of association rules," The Journal of Supercomputing, pp. 1--28, 2022.
L. a. F.-V. P. a. Y. R. a. D. I. {Shabtay, "A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data," Information Sciences, vol. 553, pp. 353--375, 2021.
I. a. R. M. a. H. M. a. N. N. a. L. F. a. N. F. Fauziah, "Market Basket Analysis with Equivalence Class Transformation Algorithm (ECLAT) For Inventory Management Using Economic Order Quantity (EOQ)," 2022.
M. J. a. G. K. Zaki, "ast vertical mining using diffsets," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 326--335.
W. a. J. M. a. M. M. a. A. Z. a. M. F. Bakar, "Postdiffset: an Eclat-like algorithm for frequent itemset mining," International Journal of Engineering & Technology, vol. 7, pp. 197--199, 2018.
L. Schmidt-Thieme, "Algorithmic Features of Eclat.," FIMI, 2004.
L. Z. J. H. A. J. A.-D. A. D. Y. A.-S. O. S. J. F. M. A. A.-A. M. F. L. Alzubaidi, "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions," Journal of big Data, vol. 8, pp. 1--74, 2021.
V.-D. a. L. C.-M. a. T. D. A. Ta, "Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading," Applied Sciences, vol. 10, no. 2, p. 437, 2020.
B. T. a. H. T. T. Khoa, "Long Short-Term Memory Recurrent Neural Network for Predicting the Return of Rate Underframe the Fama-French 5 Factor," Discrete Dynamics in Nature and Society, p. 2022, 2022.
M. a. D. Q. a. H. N. u. a. J. K. a. S. S. M. a. J. H. T. Ishfaque, "Use of recurrent neural network with long short-term memory for seepage prediction at Tarbela dam, KP, Pakistan," Energies, vol. 15, no. 9, p. 3123, 2022.
A. a. D. G. a. B. F. M. a. S. N. a. o. Bahtiar, "Data mining techniques with machine learning algorithm to predict patients of heart disease," in IOP Conference Series: Materials Science and Engineering, vol. 1088, IOP Publishing, 2021, p. 012035.
R. C. A.-M. M. C. I. Lixuandroiu, "An analysis of the impact of personality traits towards augmented reality in online shopping," Symmetry, vol. 13, no. 1, p. 416, 2021.
V. a. I. K. Umayaparvathi, "Automated feature selection and churn prediction using deep learning models," International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 3, pp. 1846--1854, 2017.
P. a. D. A. Ghadekar, "Image-Based Product Recommendations Using Market Basket Analysis," in 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), IEEE, 2019, pp. 1--5.
A. Sharma, "Retail Customer and Market Proclivity Assessment using Historical data and Social Media Analytics," 2020.
O. F. a. D. O. a. H. A. Seymen, "Customer churn prediction using deep learning," Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), pp. 520--529, 2021.