[Objective] This paper analyzes the differences in the importance of database items, aiming to address the issues of traditional association mining algorithm with redundant and worthless rules. [Methods] On the sequence with temporal constraints, we explored the non-weighted association rules with the frequency effective length and the weighting methods. Then, we used sliding window technique to study the rare weighted association rules on the time series. [Results] The accuracy of the prediction made by the proposed method increased to 69% from 62%. [Limitations] The mining algorithm took long time to extract the needed rules due to the sliding windows and the large number of rules generated. [Conclusions] The association rules of weighted time series improve the accuracy of recommendation, which also provides new directions for research method on association rules.
张勇,李树青,程永上. 基于频次有效长度的加权关联规则挖掘算法研究 *[J]. 数据分析与知识发现, 2019, 3(7): 85-93.
Yong Zhang,Shuqing Li,Yongshang Cheng. Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length. Data Analysis and Knowledge Discovery, 2019, 3(7): 85-93.
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