The proposed paper focusses on the basic concepts of association rule mining and the market basket analysis of different items. In the current study, the market analysis would be done by collecting the real, primary data directly from retailers and wholesalers. The efficiency of the FP-Growth algorithm can be measured in terms of mining of the frequent pattern. Precisely, we apply FP-Growth algorithm on the various data collected from different stores in order to trace the various association rules comprising of a basket. One discrete advantage is that it avoids the generation of candidate sets, which is computationally exhaustive. The results and conclusions drawn can be used in optimizing the market. This will help in predicting future trends and behaviours, allowing businesses to make knowledge-driven decisions.

1. Christian Borgelt, "An Implementation of the FP-growth Algorithm"
2. Sotiris Kotsiantis, DimitrisKanellopoulos, "Association Rule Mining: A Recent Overview", GESTS International Transactions on Computer Science and Engineering, Vol. 32(1), 2006, pp. 71-82
3. J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation, In: Proc. Conf. on the Management of Data (SIGMOD'00, Dallas, TX). ACM Press, New York, NY, USA 2000
4. https://en. wikipedia. org/wiki/Association_rule_learning
5. Phani Prasad, MurlidherMourya, "A Study on Market Basket Analysis Using a Data Mining Algorithm", International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Vol. 3, Issue 6, June 2013
6. Akansha Singh, K. K. Singh, Data Mining and Data Warehousing, India: Umesh Publications, 2011-2012
7. Harpreet Kaur, Kawaljeet Singh, "Market Basket Analysis of Sports Store using Association Rules", International Journal of Recent Trends in Electrical & Electronics Engg. , ISSN: 22316612, Dec. 2013
8. Neesha Sharma, C. K. Verma, "Association Rule Mining: An Overview", IJCSC, Volume 5, Number 1, March 2014, pp. 10-15, ISSN-0973-7391
9. Trnka. , "Market Basket Analysis with Data Mining Methods", International Conference on Networking and Information Technology (ICNIT), 2010
10. W Yanthy, T. Sekiya, K. Yamaguchi, "Mining Interesting Rules by Association and Classification Algorithms", FCST 09
11. Cunningham, S. J. and Frank, E. , "Market Basket Analysis of Library Circulation Data", International Conference on Neural Information Processing, Vol. 2, 1999
12. Rastogi, R. and Kyuseok Shim, "Market Optimised Association Rules with Categorical and Numerical Attributes", IEEE transactions on Knowledge and Data Engineering, Vol. 14, 2002

Market Basket Analysis Association Rule Mining Fp-tree Algorithm Frequent Itemsets Support Confidence