Title: Fast mining algorithm for multi-level association rule data under temporal constraints

Authors: Mu Yicheng

Addresses: Department of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

Abstract: Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.

Keywords: temporal constraints; association rules; data mining; feature extraction.

DOI: 10.1504/IJICT.2019.103206

International Journal of Information and Communication Technology, 2019 Vol.15 No.4, pp.431 - 444

Received: 27 Oct 2018
Accepted: 26 Nov 2018
Published online: 22 Oct 2019 *

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