Temporal frequent pattern mining is a critical area of data mining that focuses on identifying recurring set of events over time. Traditional methods often face challenges related to scalability, data complexity, and noise, necessitating the development of optimized approaches. This survey reviews the existing literature on temporal frequent pattern mining, highlights the limitations of traditional techniques, and presents recent advancements in optimized methods. The paper also discusses future directions and applications across various domains.
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Shalini et al. (2024) studied this question.
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