An Environment for Discovery of Associative Rules in Massive Datasets of Market Transactions - Matos, Helder Mateus dos Reis; Ribeiro, Wilton Freitas; Costa, João Crisóstomo Weyl Albuquerque; Santos Filho, Reginaldo Cordeiro dos (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
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.