Marginal frequent itemset mining for fault prevention of railway overhead contact system

Qian, Kaiyi; Gao, Shibin; Yu, Long

doi:10.1016/j.isatra.2021.07.018

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Moreover, as data mining evolves, there is a growing recognition of the importance of mining rare itemsets and closed itemsets in addition to frequent ones [23][24][25]. These less common but often valuable patterns have garnered attention, as they can provide unique and critical insights into the data [23][24][25].…”

Section: *Author For Correspondencementioning

confidence: 99%

Innovations in frequent itemset mining: challenges and opportunities

2023

TIS

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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.

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