Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 391-397.DOI: 10.11772/j.issn.1001-9081.2021122190

• Data science and technology • Previous Articles    

Partial periodic pattern incremental mining of time series data based on multi-scale

Yaling XUN1, Linqing WANG1, Jianghui CAI1,2(), Haifeng YANG1   

  1. 1.College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China
    2.College of Computer Science and Technology,North University of China,Taiyuan Shanxi 030051,China
  • Received:2021-12-29 Revised:2022-05-30 Accepted:2022-06-10 Online:2022-06-30 Published:2023-02-10
  • Contact: Jianghui CAI
  • About author:XUN Yaling, born in 1980, Ph. D., associate professor. Her research interests include data mining, parallel computing.
    WANG Linqing, born in 1997, M. S. candidate. His research interests include data mining, parallel computing.
    YANG Haifeng, born in 1980, Ph. D., professor. His research interests include big data mining and application.
  • Supported by:
    National Natural Science Foundation of China(62272336);Graduate Education Innovation Project of Shanxi Province(2022Y699)


荀亚玲1, 王林青1, 蔡江辉1,2(), 杨海峰1   

  1. 1.太原科技大学 计算机科学与技术学院,太原 030024
    2.中北大学 计算机科学与技术学院,太原 030051
  • 通讯作者: 蔡江辉
  • 作者简介:荀亚玲(1980—),女,山西霍州人,副教授,博士,CCF会员,主要研究方向:数据挖掘、并行计算
  • 基金资助:


Aiming at the problems of high computational complexity and poor expansibility in the mining process of partial periodic patterns from dynamic time series data, a partial periodic pattern mining algorithm for dynamic time series data combined with multi-scale theory, named MSI-PPPGrowth (Multi-Scale Incremental Partial Periodic Frequent Pattern) was proposed. In MSI-PPPGrowth, the objective multi-scale characteristics of time series data, were made full use, and the multi-scale theory was introduced in the mining process of partial periodic patterns from time series data. Firstly, both the original data after scale division and incremental time series data were used as a finer-grained benchmark scale dataset for independent mining. Then, the correlation between different scales was used to realize scale transformation, so as to indirectly obtain global frequent patterns corresponding to the dynamically updated dataset. Therefore, the repeated scanning of the original dataset and the constant adjustment of the tree structure were avoided. In which, a new frequent missing count estimation model PJK-EstimateCount was designed based on Kriging method considering the periodicity of time series to effectively estimate the frequent missing item support count in scale transformation. Experimental results show that MSI-PPPGrowth has good scalability and real-time performance. Especially for dense datasets, MSI-PPPGrowth has significant performance advantages.

Key words: frequent itemset mining, time series data, partial periodic pattern, multi-scale, incremental mining



关键词: 频繁项集挖掘, 时序数据, 部分周期模式, 多尺度, 增量挖掘

CLC Number: