Research on Association Rule Mining (ARM) focuses on developing efficient techniques to discover meaningful patterns and relationships within large and complex datasets across various domains. Recent advancements include neurosymbolic approaches like Aerial+, which use autoencoders to generate concise, high-quality rule sets while addressing rule explosion in high-dimensional data, and parallel ARM methods for handling massive datasets, such as analyzing electricity consumption in relation to weather factors using MapReduce and clustering-based discretization. Other innovations involve applying ARM to human gut microbiome analysis for disease classification, and grouping-based approaches like GARMT to optimize SQL query performance. These developments collectively enhance scalability, efficiency, and interpretability in extracting actionable insights from diverse and high-dimensional data sources.