[1]LI Hailin,LONG Fangju.Association rules analysis of time series based on synchronization frequent tree[J].CAAI Transactions on Intelligent Systems,2021,16(3):502-510.[doi:10.11992/tis.202008012]
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Association rules analysis of time series based on synchronization frequent tree

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 502-510 Column: 学术论文—知识工程 Public date: 2021-05-05
Title:
Association rules analysis of time series based on synchronization frequent tree
Author(s):
LI Hailin1; 2;  LONG Fangju1
1. Department of Information Systems, Huaqiao University, Quanzhou 362021, China;
2. Research Center of Applied Statistics and Big Data, Huaqiao University, Xiamen 361021, China
Keywords:
time series; linear segmentation; trend item-location; transactionset representation; frequent itemsets; synchronize frequent trees; association rules; time efficiency
CLC:
TP311.13
DOI:
10.11992/tis.202008012
Abstract:
In this paper, a synchronization frequent tree (SFT) algorithm is proposed to solve the problem that the classic algorithms apriori and FP-growth can not directly mine the association rules of time series data. By making use of the time attribute of time series, which has one-dimensional characteristics, we define the trend item-position representation method to represent the time series data, construct a basic tree for the first time series, and then find the information between the leaf nodes of the tree and the list items by intersection, and then judge whether the item and all the nodes in the branch constitute a frequent K itemsets. In the SFT algorithm, the memory occupancy of the data represented by the trend item-location is better than that of the original data, and candidate frequent itemsets will not be generated during the mining process, which makes the algorithm show better time performance in the entire mining process. Numerical experiments based on commodity data and stock data show that the results of the SFT algorithm are consistent with the results of the comparison algorithm, and what’s more, in all levels of data, its time complexity is better than that of the comparison algorithm.
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