Market Basket Analysis using Apriori Algorithm

Yojna Arora; Neha Bhateja; Vanshita Goswami; Rohan Kukreja; Amisha Rajput

doi:10.55524/

Authors

Yojna Arora Assistant Professor, Computer Science Engineering, ASET, Amity University Haryana, India Author
Neha Bhateja Assistant Professor, Computer Science Engineering, ASET, Amity University Haryana, India Author
Vanshita Goswami Student, Computer Science Engineering, ASET, Amity University Haryana, India Author
Rohan Kukreja Student, Computer Science Engineering, ASET, Amity University Haryana, India Author
Amisha Rajput Student, Computer Science Engineering, ASET, Amity University Haryana, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Data mining, market basket analysis, Apriori, Association

Abstract

A Technique that check for dependency for one Data item to another is Association Rule which is an old Data mining approach. Which is used to identify the next product that might interest a customer. The Apriori Algorithm is applied in this for mining frequent products sets and relevant Association rule. With this algorithm we can use this for up-sell and also in cross-sell to show the Association rule with the help of the algorithm. These methods are widely used in global companies, so for the good understanding the companies used the methods to remain up to date that what customers demands with which products. The results helps the big retailers to identify a trend for customers buying patterns, which is very helpful information for the retailers to plan their big business operations.

Downloads

Download data is not yet available.

References

Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify the changing trends of market data using association rule mining. Procedia computer science, 85, 78-85.

Chen, Y. L., Tang, K., Shen, R. J., & Hu, Y. H. (2005). Market basket analysis in a multiple store environment. Decision support systems, 40(2), 339-354.

Raorane, A. A., Kulkarni, R. V., & Jitkar, B. D. (2012). Association rule–extracting knowledge using market basket analysis. Research Journal of Recent Sciences ISSN, 2277, 2502.

Hilage, T. A., & Kulkarni, R. V. (2011). Application of data mining techniques to a selected business organization with special reference to buying behaviour. International Journal of Database Management Systems, 3(4), 169.

Arora, Y., Singhal, A., & Bansal, A. (2014). A study of applications of RBF network. International Journal of Computer Applications, 94(2).

Arora, Y., Singhal, A., & Bansal, A. (2014). PREDICTION & WARNING: a method to improve student's performance. ACM SIGSOFT Software Engineering Notes, 39(1), 1-5.

Sharma, N., Arora, Y., Makkar, P., Sharma, V., & Gupta, H. (2021). Real Estate Price’s Forecasting through Predictive Modelling. In Machine Learning for Predictive Analysis (pp. 589-597). Springer, Singapore.

Arora, Y., & Kohli, R. (2022). A Study for Making Universities Intelligent Using Data Analytics and Artificial Intelligence. In Intelligent System Algorithms and Applications in Science and Technology (pp. 147-155). CRC Press.

Musungwini, S., Zhou, T. G., Gumbo, R., & Mzikamwi, T. (2014). The relationship between (4ps) & market basket analysis: a case study of grocery retail shops in Gweru Zimbabwe.