Review of Frequent Temporal Pattern Mining Methods

被引：0
|
作者：
Tang, Zengjin [1 ,2 ]
Xu, Zhenshun [1 ,2 ]
Su, Mengyao [1 ,2 ]
Liu, Na [1 ,2 ]
Wang, Zhenbiao [1 ,2 ]
Zhang, Wenhao [1 ,2 ]
机构：
[1] College of Compute Science and Engineering, North Minzu University, Yinchuan,750021, China
[2] The Key Laboratory of Images, Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan,750021, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 17期
关键词：
Time series;
D O I：
10.3778/j.issn.1002-8331.2403-0114
中图分类号：
学科分类号：
摘要：
Frequent temporal pattern mining refers to the process of discovering frequently occurring patterns or patterns from time series data. Its purpose is to help understand important features in time series data, such as periodicity, trends, and anomalies, which can help predict future development trends and identify abnormal situations. Based on literature research on frequent temporal pattern mining methods in recent years, they are divided into three categories according to key technologies and representative algorithms, namely structural constraint based frequent temporal pattern mining methods, parameter constraint based frequent temporal pattern mining methods, and window based frequent temporal pattern mining methods. Firstly, the background of frequent temporal pattern mining methods and the characteristics of each method are described. Secondly, the development and classification of three mining methods are introduced, and a detailed comparative analysis is conducted on the advantages, disadvantages, and performance of each improved method. Finally, the frequent temporal pattern mining methods are summarized and summarized, and the future research directions of frequent temporal pattern mining methods are discussed. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
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页码：48 / 61
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