Research on Improved Apriori Algorithm based on MapReduce and HBase

被引：0
|
作者：
Feng, Dongyu [1 ,2 ]
Zhu, Ligu [1 ,2 ]
Zhang, Lei [1 ,2 ]
机构：
[1] Commun Univ China, Coll Comp, Beijing, Peoples R China
[2] Beijng Key Lab Big Data Secur & Protect Ind, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2016 IEEE ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC 2016) | 2016年
关键词：
Apriori; MapReduce; HBase; data mining; association rules;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve the efficiency of Apriori algorithm for mining frequent item sets, MH-Apriori algorithm was designed for big data to address the poor efficiency problem. MH-Apriori takes advantages of MapReduce and HBase together to optimize Apriori algorithm. Compared with the improved Apriori algorithm simply based on MapReduce framework, timestamp of HBase is utilized in this algorithm to avoid generating a large number of key/value pairs. It saves the pattern matching time and scans the database only once. Also, to obtain transaction marks automatically, transaction mark column is added to set list for computing support numbers. MH-Apriori was executed on Hadoop platform. The experimental results show that MH-Apriori has higher efficiency and scalability.
引用
收藏
页码：887 / 891
页数：5
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