Efficiency and Effectiveness in Utility-Based and Frequent Itemset Mining: A Comprehensive Review

Abstract

The rapid growth of data in various domains has led to the need for efficient pattern mining algorithms that can handle large-scale datasets. In this study, the comprehensive review in the domains of utility-based and frequent itemset mining (FIM), focusing on the dual facets of efficiency and effectiveness. In the ever-expanding landscape of data mining and knowledge discovery, the extraction of valuable patterns and insights from large datasets is paramount. Utility-based mining addresses the challenge of incorporating user-defined measures of importance, reflecting real-world applications where not all items are equal. Simultaneously, FIM seeks to identify recurring patterns within datasets, providing valuable associations and dependencies. This review synthesizes recent advancements and methodologies in both utility-based and FIM, analyzing their respective strengths and limitations. Efficiency considerations encompass algorithmic optimizations, parallel computing, and scalability, ensuring that the mining process is computationally tractable for large-scale datasets. Effectiveness evaluations delve into the quality of discovered patterns, emphasizing their relevance and utility in diverse applications. The synthesis of these two mining paradigms underscores the importance of striking a balance between computational efficiency and the ability to extract meaningful patterns. By examining state-of-the-art techniques and methodologies, this review aims to provide researchers, practitioners, and decision-makers with a comprehensive understanding of the current landscape in utility-based and FIM, offering insights into future directions for advancing the efficiency and effectiveness of pattern discovery in diverse domains.