A Comprehensive Review of Association Rule Mining Based on Evolutionary Computing

This paper highlights the significance of association rule mining (ARM) in identifying common patterns in data mining across diverse domains like shopping, computer networks, recommendation systems, and healthcare. Recently, ARM leveraging evolutionary computing has gained prominence as a crucial research avenue, addressing computational time constraints in traditional ARM approaches. The study presents a thorough review of ARM employing evolutionary computing, focusing on the application of evolutionary algorithms to explore association rules (ARs). Through the integration of interactive and adaptive methods, the research enhances communication among variables, encourages collective learning, and refines the exploration process, resulting in a collection of representative ARs offering optimal solutions. The paper contributes substantially to the ARM field, providing a detailed perspective on the efficacy of evolutionary computing in revealing hidden information within datasets. Additionally, the paper addresses key research questions concerning current methods, recent advancements, challenges, and difficulties in ARM-based evolutionary computation.