Modified Genetic Algorithm and Association Rule Mining for the Retail Sector

  • Piyush Vyas Texas A&M University-Central Texas, United States
  • Aditya Nagdiya Devi Ahilya Vishwavidyalaya, India
Keywords: Association Rule Mining, Apriori Algorithm, Genetic algorithm, Data Mining

Abstract

This paper concentrates on the optimization of elementary association rule mining. The basic approach of association rule mining generates the positive association rule but focusing on both positive and negative association rule mining to find out efficient results is lacking. Thus our aim is to provide an approach to optimize all positive and negative association rules with the help of a modified genetic algorithm. A genetic algorithm is an optimization technique that provides the best possible solutions that are stronger than the other solutions. The present approach focuses on the importance of population through mean fitness value for further genetic algorithm operation. This paper also shows a comparison between normal Apriori, the Genetic Algorithm, and our proposed algorithm. Where in as a result the proposed approach worked better than others. We believe that the proposed methodology would increase the efficiency of the Decision support system of retail stores.

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References

E. Ramaraj and N. Venkatesan, “Positive and negative association rule analysis in health care database,” Int. J. Comput. Sci. Netw. Secur., vol. 8, no. 10, pp. 325–330, 2008.

M. A. Mahdi, K. M. Hosny, and I. Elhenawy, “FR-Tree: A novel rare association rule for big data problem,” Expert Syst. Appl., vol. 187, p. 115898, 2022.

R. V. Prakash, D. Govardhan, and D. S. Sarma, “Mining frequent itemsets from large data sets using genetic algorithms,” Artif. Intell. Tech. Approaches & Pract. Appl., no. 4 SPEC. ISSUE, pp. 38–43, 2011.

A. Sohail, “Genetic algorithms in the fields of artificial intelligence and data sciences,” Ann. Data Sci., vol. 10, no. 4, pp. 1007–1018, 2023.

R. Agrawal, R. Srikant, and others, “Fast algorithms for mining association rules,” in Proc. 20th int. conf. very large data bases, VLDB, 1994, pp. 487–499.

M. Saggar, A. K. Agrawal, and A. Lad, “Optimization of association rule mining using improved genetic algorithms,” in 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), 2004, pp. 3725–3729.

C. Cornelis, P. Yan, X. Zhang, and G. Chen, “Mining positive and negative association rules from large databases,” in 2006 IEEE Conference on Cybernetics and Intelligent Systems, 2006, pp. 1–6.

H. Zhu and Z. Xu, “An effective algorithm for mining positive and negative association rules,” in 2008 International Conference on Computer Science and Software Engineering, 2008, pp. 455–458.

R. Dewang and J. Agarwal, “A new method for generating all positive and negative association rules,” Int. J. Comput. Sci. Eng., vol. 3, no. 4, pp. 1649–1657, 2011.

S. Das and B. Saha, “Data quality mining using genetic algorithm,” Int. J. Comput. Sci. Secur., vol. 3, no. 2, pp. 105–112, 2009.

S. N. Deepa and S. N. Sivanandam, “Principles of soft computing.” Delhi, India: Wiley India Pvt. Ltd, 2011.

M. Anandhavalli, S. K. Sudhanshu, A. Kumar, and M. K. Ghose, “Optimized association rule mining using genetic algorithm,” Adv. Inf. Mining, ISSN, vol. 9753265, 2009.

S. Ghosh, S. Biswas, D. Sarkar, and P. P. Sarkar, “Mining frequent itemsets using genetic algorithm,” arXiv Prepr. arXiv1011.0328, 2010.

P. Bajpai and M. Kumar, “Genetic algorithm--an approach to solve global optimization problems,” Indian J. Comput. Sci. Eng., vol. 1, no. 3, pp. 199–206, 2010.

P. Vyas and J. Dubey, “An Efficient Methodological Study for Optimization of Negative Association Rule Mining,” IJCA, ICRTITCS, vol. 3, pp. 27–31, 2013.

P. Vyas and A. Chauhan, “Comparative optimization of efficient association rule mining through PSO and GA,” Proc. - 2013 Int. Conf. Mach. Intell. Res. Adv. ICMIRA 2013, pp. 258–263, Oct. 2014, doi: 10.1109/ICMIRA.2013.55.

P. Vyas, K. N. M. Ragothaman, A. Chauhan, and B. P. Rimal, “Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach,” AMCIS 2022 Proc., Aug. 2022, Accessed: Dec. 08, 2022.

P. Vyas, K. Ragothaman, A. Chauhan, and B. Rimal, “Classification of COVID-19 Cases: An Exploratory Study by Incorporating Transfer Learning with Cloud,” MWAIS 2021 Proc., May 2021.

P. Vyas, G. Vyas, and A. Chennamaneni, “Detection of Malicious Bots on Twitter through BERT Embeddings-based Technique,” in AMCIS 2023 Proceedings, p. 6, 2023.

P. Vyas, J. Liu, and O. El-Gayar, “Fake News Detection on the Web: An LSTM-based Approach,” AMCIS 2021 Proc., Aug. 2021.

Published
2023-09-11
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How to Cite
Vyas, P., & Nagdiya, A. (2023). Modified Genetic Algorithm and Association Rule Mining for the Retail Sector. Journal of Information Systems and Informatics, 5(3), 1099-1110. https://doi.org/10.51519/journalisi.v5i3.561