[1]李德辰,吕一帆,赵学健.一种基于预判筛选的频繁项集挖掘算法[J].计算机技术与发展,2018,28(05):99-102.[doi:10.3969/ j. issn.1673-629X.2018.05.023]
 LI De-chen,LYU Yi-fan,ZHAO Xue-jian.A Frequent Item-set Mining Algorithm Based on Prejudgment and Screening[J].,2018,28(05):99-102.[doi:10.3969/ j. issn.1673-629X.2018.05.023]
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一种基于预判筛选的频繁项集挖掘算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
99-102
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
A Frequent Item-set Mining Algorithm Based on Prejudgment and Screening
文章编号:
1673-629X(2018)05-0099-04
作者:
李德辰1 吕一帆1 赵学健2
1. 南京邮电大学 物联网学院,江苏 南京 210023;
2. 南京邮电大学 现代邮政学院,江苏 南京 210003
Author(s):
LI De-chen1 LYU Yi-fan1 ZHAO Xue-jian2
1. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;
2. School of Modern Post,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
关联规则Apriori数据挖掘预判筛选频繁项集
Keywords:
association rulesAprioridata miningprejudging and screeningfrequent item-set
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.05.023
文献标志码:
A
摘要:
频繁项集挖掘作为关联规则挖掘技术的关键步骤,其性能对关联规则挖掘具有重要的意义。 针对经典关联规则挖掘算法———Apriori 算法存在的产生候选项目集效率低和频繁扫描数据库等缺点,对 Apriori 算法的原理及效率进行分析,提出一种基于预判筛选策略的频繁项集挖掘算法。 该算法通过对原始数据集的随机取样,得出样本频繁项集的支持度集合来进行预判筛选,从而对原始数据集候选项集进行二次剪枝,并且引入阻尼因子和补偿因子对预判筛选产生的误差进行修正,以保证算法的误判率和遗漏率。 实验结果表明,该算法具有更好的时效性。
Abstract:
Frequent item-set mining as a key step in mining association rules,its performance is of great significance to mining association rules. Aiming at the shortcomings of classical Apriori algorithm like low efficiency and frequent scanning database,we propose a frequent item-set mining algorithm based on prejudge and screening through analysis of the principle and efficiency of the Apriori algorithm. It obtains the support-set of frequent item-set for prejudgment and screening by random sampling of the original dataset,so as to make the second pruning of the candidate set from original dataset. The damping factor and the compensation factor are introduced to correct the error caused by the pre-selection screening to ensure the misjudgment rate and the omission rate of the algorithm. The experiments show that the proposed algorithm has better time efficiency.

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更新日期/Last Update: 2018-07-02