| 引用本文: | 管博伦,张立平,董 伟,李闰枚,朱静波,孔娟娟,汪 焱.基于注意力机制的高效关联规则算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（3）：99-107 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 针对事务间相似度高且频繁出现的数据集,在频繁项集挖掘过程中产生的大量冗余,分析部分已有关
联规则算法在挖掘频繁项集方面的不足,提出一种使用注意力机制进行剪枝的关联规则挖掘算法。 方法 该算法结
合垂直数据格式和注意力机制的优点,依次求出频繁 k 项集中的注意力权重,利用注意力权重过滤掉交集计算中
的冗余,生成精简频繁项集,最后将精简频繁项集和注意力权重进行合并,得到频繁项集。 结果 实验结果表明:在
Data 数据集上该算法比 Eclat、 Apriori、 FP-Growth 的时间最大提升 94. 6%、73%、95. 5%,比 FP -Growth 最多节省
空间 61. 991MB;在 Accident 数据集上,该算法比 Eclat、 Apriori、 FP-Growth 的时间最大提升 93. 4%、69%、85. 7%,
比 FP-Growth 最多节省空间 58. 786 MB。 结论 通过引入注意力机制增强了算法的泛化能力和稳定性,减少了挖掘
频繁项集产生的冗余,提高了算法速度,降低了挖掘过程中内存的开销,尤其是在 AFI 指数较大、支持度较低的数
据集上,该算法在时间和空间开销上更具备优势。 |
| 关键词: 关联规则 频繁项集 冗余 注意力机制 精简频繁项集 |
| DOI： |
| 分类号: |
| 基金项目: |
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| An Efficient Association Rule Algorithm Based on Attention Mechanism |
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GUAN Bolun ZHANG Liping DONG Wei LI Runmei ZHU Jingbo KONG Juanjuan WANG Yan
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Institute of Agricultural Economics and Information Anhui Academy of Agricultural Sciences Hefei 230001 China
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| Abstract: |
| Objective Aiming at the large amount of redundancy generated during the mining process of frequent item sets
in datasets with high similarity and frequent occurrence among transactions this paper analyzes the deficiencies of some
existing association rule algorithms in mining frequent item sets and proposes an association rule mining algorithm that uses
an attention mechanism for pruning. Methods This algorithm combined the advantages of the vertical data format and the
attention mechanism. It sequentially calculated the attention weights in frequent k-item sets used these weights to filter
out the redundancy in intersection calculations generated simplified frequent item sets and finally merged the simplified
frequent item sets with the attention weights to obtain the frequent item sets. Results The experimental results showed
that on the Data dataset the proposed algorithm improved time efficiency by up to 94. 6% 73. 0% and 95. 5% compared
with Eclat Apriori and FP-Growth respectively it also saved up to 61. 991 MB of space compared with FP-Growth. On
the Accident dataset it achieved maximal time efficiency improvements of 93. 4% 69. 0% and 85. 7% over the same
three baselines respectively with a maximum space saving of 58. 786 MB against FP-Growth. Conclusion By
introducing the attention mechanism the generalization ability and stability of the algorithm are enhanced the redundancy
generated during frequent item set mining is reduced the algorithm speed is improved and the memory overhead during the mining process is lowered. Especially for datasets with a large AFI index and low support this algorithm has more
advantages in terms of time and space consumption. |
| Key words: association rule frequent item set redundancy attention mechanism simplified frequent item set |
