Advancements and Applications in Association Rule Mining A Review of Key Algorithms and Future Directions (original) (raw)
Association rule mining is a crucial data mining technique used to uncover relationships between variables in large datasets. This paper provides a comprehensive review of various association rule algorithms, including Apriori, FP-Growth, ECLAT, AIS, and SETM. Each algorithm is discussed in terms of its methodology, advantages, and limitations. The paper also explores advanced extensions such as Multi-Level and Multi-Dimensional Association Rules, which offer deeper insights by incorporating hierarchical and multi-attribute dimensions into the analysis. By examining the evolution of these techniques, the paper highlights ongoing challenges, such as scalability, efficiency, and interpretability, and suggests future research directions, including the integration of association rule mining with other data mining techniques and the development of algorithms for complex data types. This review aims to provide a detailed understanding of the state-of-the-art in association rule mining and its practical applications.
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