Abstract

The era of big data, characterized by vast and complex datasets, has prompted the need for advanced data mining techniques. Frequent itemset mining, a fundamental method in data mining, plays a pivotal role in uncovering hidden knowledge and patterns. However, it faces challenges in scalability, adaptability to uncertainty, and the need to consider rare and closed itemsets. This paper reviews recent advancements in frequent itemset mining, focusing on innovative approaches introduced in 2022 and 2023. These advances address dynamic database updates, efficient fault prediction, scalability issues, quantitative pattern mining, utility-based approaches, mining rare itemsets, real-time decision-making, and uncertain frequent itemset mining. While these studies offer valuable solutions, they also present challenges related to scalability, adaptability, and performance. Future research should refine these methods to meet evolving data mining demands.

Keyword

Frequent itemset mining, Data Mining, Decision making, Scalability.

Cite this article

Behera A, Zuber M

Refference

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