An Environment for Discovery of Associative Rules in Massive Datasets of Market Transactions
Resumo
Associative Rule Mining is a data mining technique to extract sets of elements frequently associated with each other, originally developed in Market Basket Analysis (MBA) settings. A major concern with MBA associative rule mining is the availability of computational resources needed to process large collections of data, especially in time-dependent domains like markets. A knowledge-extraction-based environment is proposed to accommodate best practices to process massive MBA datasets, along with use cases of algorithms dedicated to generating associative rules. Market companies can adopt this environment to enhance marketing strategies, improve inventory management, and optimize business rules for maximum profit.
Palavras-chave:
Market Basket Analysis, Retail, Associative Rule Learning
Referências
Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207–216.
Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, page 487–499, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, SIGMOD ’97, page 255–264, New York, NY, USA. Association for Computing Machinery.
Celik, O., Hasanbasoglu, M., Aktas, M. S., and Kalipsiz, O. (2020). Association Rule Mining on Big Data Sets. In Birant, D., editor, Data Mining, chapter 3. IntechOpen, Rijeka.
Gino, H., Pedro, D., Ponciano, J., Linhares, C., and Traina, A. (2023). Exploratory analysis on market basket data using network visualization. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 19–30, Porto Alegre, RS, Brasil. SBC.
Grahne, G. and Zhu, J. (2003). Efficiently Using Prefix-trees in Mining Frequent Itemsets. In Workshop on Frequent Itemset Mining Implementations.
Han, J., Pei, J., Yin, Y., and Mao, R. (2004). Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery, 8(1):53–87.
Kiani, G. H. (2020). Determining profitable products in the retail market with consideration of cash limitation and exhibition periods. Journal of Retailing and Consumer Services, 55:102079.
Leskovec, J., Rajaraman, A., and Ullman, J. D. (2019). Mining of Massive Datasets. Cambridge University Press, third edition.
Matos, H., Ribeiro, W., dos Santos Filho, R., and Costa, J. (2024). Descoberta de Padrões e Tendências de Compras em Supermercados Utilizando Análise de Cestas de Compras. In CONTECSI USP - International Conference on Information Systems and Technology Management - ISSN 2448-1041.
Mohapatra, D., Tripathy, J., Mohanty, K. K., and Nayak, D. S. K. (2021). Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE.
Pei, J., Han, J., Lu†, H., Nishio, S., Tang, S., and Yang, D. (2007). H-Mine: Fast and space-preserving frequent pattern mining in large databases. IIE Transactions, 39(6):593–605.
Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases.
Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. The Journal of Open Source Software, 3(24).
Schonhorst, G., Paes, V., Balestrassi, P., Paiva, A., and Campos, P. (2017). Data mining association rules applied to supermarket transactional data modeling: a case study in brazil. In International Joint Conference - ICIEOM-ADINGOR-IISE-AIM-ASEM (IJC 2017).
Yan, X., Zhang, C., and Zhang, S. (2009). Confidence metrics for association rule mining. Applied Artificial Intelligence, 23(8):713–737.
Yoseph, F. and Heikkilä, M. (2020). A new approach for association rules mining using computational and artificial intelligence. Journal of Intelligent & Fuzzy Systems, 39(5):7233–7246.
Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, page 487–499, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, SIGMOD ’97, page 255–264, New York, NY, USA. Association for Computing Machinery.
Celik, O., Hasanbasoglu, M., Aktas, M. S., and Kalipsiz, O. (2020). Association Rule Mining on Big Data Sets. In Birant, D., editor, Data Mining, chapter 3. IntechOpen, Rijeka.
Gino, H., Pedro, D., Ponciano, J., Linhares, C., and Traina, A. (2023). Exploratory analysis on market basket data using network visualization. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 19–30, Porto Alegre, RS, Brasil. SBC.
Grahne, G. and Zhu, J. (2003). Efficiently Using Prefix-trees in Mining Frequent Itemsets. In Workshop on Frequent Itemset Mining Implementations.
Han, J., Pei, J., Yin, Y., and Mao, R. (2004). Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery, 8(1):53–87.
Kiani, G. H. (2020). Determining profitable products in the retail market with consideration of cash limitation and exhibition periods. Journal of Retailing and Consumer Services, 55:102079.
Leskovec, J., Rajaraman, A., and Ullman, J. D. (2019). Mining of Massive Datasets. Cambridge University Press, third edition.
Matos, H., Ribeiro, W., dos Santos Filho, R., and Costa, J. (2024). Descoberta de Padrões e Tendências de Compras em Supermercados Utilizando Análise de Cestas de Compras. In CONTECSI USP - International Conference on Information Systems and Technology Management - ISSN 2448-1041.
Mohapatra, D., Tripathy, J., Mohanty, K. K., and Nayak, D. S. K. (2021). Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE.
Pei, J., Han, J., Lu†, H., Nishio, S., Tang, S., and Yang, D. (2007). H-Mine: Fast and space-preserving frequent pattern mining in large databases. IIE Transactions, 39(6):593–605.
Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases.
Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. The Journal of Open Source Software, 3(24).
Schonhorst, G., Paes, V., Balestrassi, P., Paiva, A., and Campos, P. (2017). Data mining association rules applied to supermarket transactional data modeling: a case study in brazil. In International Joint Conference - ICIEOM-ADINGOR-IISE-AIM-ASEM (IJC 2017).
Yan, X., Zhang, C., and Zhang, S. (2009). Confidence metrics for association rule mining. Applied Artificial Intelligence, 23(8):713–737.
Yoseph, F. and Heikkilä, M. (2020). A new approach for association rules mining using computational and artificial intelligence. Journal of Intelligent & Fuzzy Systems, 39(5):7233–7246.
Publicado
17/11/2024
Como Citar
MATOS, Helder Mateus dos Reis; RIBEIRO, Wilton Freitas; COSTA, João Crisóstomo Weyl Albuquerque; SANTOS FILHO, Reginaldo Cordeiro dos.
An Environment for Discovery of Associative Rules in Massive Datasets of Market Transactions. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 508-519.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245197.
