[1]伊瑶瑶,茅苏. Hadoop下的关联规则分析研究[J].计算机技术与发展,2015,25(09):84-88.
　YI Yao-yao,MAO Su. Research on Association Rules Analysis under Hadoop Platform[J].,2015,25(09):84-88.
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 Hadoop下的关联规则分析研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年09期
页码:
84-88
栏目:
智能、算法、系统工程
出版日期:
2015-09-10

文章信息/Info

Title:
 Research on Association Rules Analysis under Hadoop Platform
文章编号:
1673-629X（2015)09-0084-05
作者:
 伊瑶瑶; 茅苏
 1.南京邮电大学 计算机学院；2.江苏省无线传感网高技术研究重点实验室；3.宽带无线通信与传感网技术教育部重点实验室
Author(s):
 YI Yao-yao; MAO Su
关键词:
 ; 关联规则; 支持度; 置信度; 频繁项集
Keywords:
association rules; support degree; confidence degree; frequent item sets
分类号:
TP301.6
文献标志码:
A
摘要:
 Apriori算法是关联规则挖掘中最基本也是最重要的算法之一。但现有的Apriori算法存在对数据库的扫描次数过多，产生了大量的候选项集合，算法执行效率较低，运行时间显著增加等问题。文中针对现有Apriori扫描数据库过于频繁的问题，在Hadoop平台下对Apriori算法进行改进，提出一种改进算法H-Apriori算法。利用并行方法计算频繁项集，该算法将原始数据集按字母排序，减少频繁项集的计算开销，避免反复扫描数据库带来的时间上的消耗，从而提高算法的执行效率。通过与传统Apriori算法的执行时间相比较，实验结果表明，提出的改进算法H-Apriori明显减少了访问数据库的时间，有较高的执行效率。
Abstract:
 Apriori algorithm is one of the most basic and important algorithms in association rules mining. But the existing Apriori algo-rithm scans database many times,generating a large number of candidate item sets,having a low execution efficiency,increasing running time significantly. Based on existing Apriori algorithm scanning database many times,put forward a new method,H-Apriori,which calcu-lates frequent items of association rules under Hadoop platform for improving efficiency of algorithm,reducing time accessing database, and sorts the original datasets,reducing frequent datasets’ computing cost,avoiding the time consumption by repeated scanning database, improving the efficiency of the algorithm. The experimental results show that the improved algorithm H-Apriori significantly reduces the amount of time to access the database with high execution efficiency.

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更新日期/Last Update: 2015-10-16