A Regional Periodic Pattern Mining Algorithm Based on Apriori-like Principle

LI Peng-yue; HE Jie; TAN Zhi-guo

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A Regional Periodic Pattern Mining Algorithm Based on Apriori-like Principle

更新时间：2026-09-04

- A Regional Periodic Pattern Mining Algorithm Based on Apriori-like Principle
- Vol. 35, Issue 9, Pages: 85-89(2018)
- 作者机构：
  
  1. 陆军军医大学(第三军医大学机构 ) 生物医学工程系医学图像学教研室,重庆,400038
  2. 国防科技大学计算机学院,湖南,长沙,410073
  3. 武警警官学院信息通信系,四川,成都,610213
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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李鹏岳, 何杰, 谭志国. 基于类Apriori原则的区域周期模式挖掘算法[J]. 微电子学与计算机, 2018, 35(9): 85-89.

LI Peng-yue, HE Jie, TAN Zhi-guo. A Regional Periodic Pattern Mining Algorithm Based on Apriori-like Principle[J]. 2018, 35(9): 85-89.
李鹏岳, 何杰, 谭志国. 基于类Apriori原则的区域周期模式挖掘算法[J]. 微电子学与计算机, 2018, 35(9): 85-89. DOI：

LI Peng-yue, HE Jie, TAN Zhi-guo. A Regional Periodic Pattern Mining Algorithm Based on Apriori-like Principle[J]. 2018, 35(9): 85-89. DOI：

摘要

时间序列在各领域广泛存在

针对目前已有的周期模式难以描述仅在时间序列部分区域存在的周期行为的现状

提出了区域周期模式的概念.在对此区域周期模式进行详细形式化描述的基础上

进一步提出了一种基于类Apriori原则的区域周期模式挖掘算法.该算法能在保证挖掘结果完整性的同时

较大程度地提高挖掘效率.通过在网络安全公开数据集上的实验表明

该算法能快速精确地挖掘出时间序列中所有的区域周期模式.

Abstract

Time series exists widely in various fields. In view of the fact that the existing periodic models are difficult to describe periodic behaviors only activate in partial regions of time series

a novel concept called regional periodic pattern is proposed. After formalizing this regional periodic pattern

a regional periodic pattern mining algorithm based on apriori-like principle is further proposed

which can greatly improve the efficiency of mining under the premise of ensuring the integrity of the mining results. Experiments on a public dataset of network security show that the proposed algorithm can quickly and accurately mine all regional periodic patterns in time series.

关键词

Keywords

references

Berlin Heidelberg, Springer Qiao Y, Yang Y, He J, et al. Detecting parasite P2P botnet in eMule-like networks through quasi-periodicity recognition[M]. Berlin Heidelberg, Springer, 2012:127-139. DOI: 127

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张玲, 陈硕, 张旸.基于自相关获取周期的时间序列模式挖掘算法[J].信息工程大学学报, 2015, 16(2):209-213. DOI: 209

Agrawal R, Srikant R. Fast algorithms for mining association rules[C]// Very Large Data Bases Conference, Santiago de Chile, 1994, 23 (3) : 487-499.

Han J, Yin Y, Dong G. Efficient mining of partial periodic patterns in time series database[C]// International Conference on Data Engineering, 1999. Los Angeles, USA, IEEE Xplore, 1999: 106-115.

Sheng C, Hsu W, Lee M L. Mining dense periodic patterns in time series data[J]. IEEE Transactions on Knowledge % Data Engineering, 2003, 15(3):613-628. DOI: 613

Zhang D, Lee K, Lee I, et al. Hierarchical trajectory clustering for spatio-temporal periodic pattern mining[J]. Expert Systems with Applications, 2018: 92. DOI: 92

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