[1]罗章铭,唐 杰,黄逸奇,等.基于二进制编码的 Apriori 增量更新算法研究[J].计算机技术与发展,2022,32(01):47-53.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 009]
　LUO Zhang-ming,TANG Jie,HUANG Yi-qi,et al.Research on Apriori Incremental Update Improved AlgorithmBased on Binary Code[J].,2022,32(01):47-53.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 009]
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基于二进制编码的 Apriori 增量更新算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年01期
页码:
47-53
栏目:
大数据分析与挖掘
出版日期:
2022-01-10

文章信息/Info

Title:
Research on Apriori Incremental Update Improved AlgorithmBased on Binary Code
文章编号:
1673-629X(2022)01-0047-07
作者:
罗章铭; 唐 杰; 黄逸奇; 张 锦
湖南师范大学 信息科学与工程学院,湖南 长沙 410006
Author(s):
LUO Zhang-ming; TANG Jie; HUANG Yi-qi; ZHANG Jin
School of Information Science and Engineering,Hunan Normal University,Changsha 410006,China
关键词:
数据挖掘; Apriori 算法; 关联规则; 二进制; 增量更新
Keywords:
data mining; Apriori algorithm; association rules; binary; incremental update
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 01. 009
摘要:
针对经典 Apriori 算法在迭代过程中频繁扫描数据库,且动态数据更新后需要重新处理数据的不足,提出一种基于二进制编码的增量更新改进 CBEF-Apriori 算法。 该算法的核心思想是将添加增量后的项集、事务转换成二进制编码,从而将计算项集支持度转化为项集与事务数据库的二进制编码位运算过程。 改进算? 法筛选原数据库生成的频繁项集与增量数据库新生成的候选项集,有效减少了候选项集的规模,提高算法效率的同时更符合现实需要。 实验结果表明,相比于经 典 Apriori 算法和 CBE-Apriori 算法,改进算法在挖掘出正确频繁项集的数量不降低的情况下,明显提升了计算效率,在小数据规模下相比经典 Apriori 算法最高提升 3. 6 倍,相比 CBE-Apriori 算法最高提升 1. 4 倍。 在较大数据规模下相比经典 Apriori 算法最高提升 10. 41 倍,相比 CBE-Apriori 算法最高提升 11. 53 倍。
Abstract:
Aiming at the defect that the classic Apriori algorithm frequently scans the database during the iterative process,and the dataneeds to be reprocessed? ? ?after the dynamic data is updated,an improved CBEF - Apriori algorithm based on the incremental update ofbinary coding is proposed. The core idea of? ?the improved algorithm is to convert the added itemsets and transactions into binary codes,soas to convert the calculation itemsets support into the binary code bit operation process of the itemsets and the transaction database. Theimproved algorithm filters the frequent itemsets generated by the original database and the candidate item sets newly generated by the in鄄cremental database,which effectively reduces the size of the candidate item sets,improves the efficiency of the algorithm,and meets actualneeds. The experiment shows that compared with the classic Apriori algorithm and the CBE-Apriori algorithm,the improved algorithmmines the number of correct frequent itemsets without reducing,and its computational efficiency is significantly improved, which is 3. 6times higher than the classic Apriori algorithm at a small data scale. Under a larger data scale,it is up to 10. 41 times higher than theclassic Apriori algorithm,and up to 11. 53 times higher than the CBE-Apriori algorithm.

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更新日期/Last Update: 2022-01-10