计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (17): 48-61.DOI: 10.3778/j.issn.1002-8331.2403-0114

• 热点与综述 • 上一篇    下一篇

频繁时序模式挖掘方法综述

唐增金,徐贞顺,苏梦瑶,刘纳,王振彪,张文豪   

  1. 1.北方民族大学 计算机科学与工程学院,银川 750021
    2.北方民族大学 图像图形智能处理国家民委重点实验室,银川 750021
  • 出版日期:2024-09-01 发布日期:2024-08-30

Review of Frequent Temporal Pattern Mining Methods

TANG Zengjin, XU Zhenshun, SU Mengyao, LIU Na, WANG Zhenbiao, ZHANG Wenhao   

  1. 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
  • Online:2024-09-01 Published:2024-08-30

摘要: 频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,其目的是可以帮助理解时间序列数据中的重要特征,例如周期性、趋势和异常等,有助于预测未来的发展趋势和识别异常情况等。根据近年来的频繁时序模式挖掘方法的相关文献调研,按照关键技术和代表性算法将其分为三类,即基于结构约束的频繁时序模式挖掘方法、基于参数约束的频繁时序模式挖掘方法和基于窗口的频繁时序模式挖掘方法。陈述了频繁时序模式挖掘方法的背景以及各方法的特点;分别介绍了三类挖掘方法的发展以及分类,并从优缺点和性能等方面对各类改进方法进行了详细的对比分析;对频繁时序模式挖掘方法进行归纳和总结,并对频繁时序模式挖掘方法的未来研究方向进行了展望。

关键词: 时序数据, 频繁时序模式, 结构约束, 参数约束, 窗口, 数据挖掘

Abstract: 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.

Key words: time series data, frequent temporal patterns, structural constraints, parameter constraints, window, data mining